Popisi

Column

Svi studenti

Svi studenti
id Ime Prezime Status mail DZ KVIZ ESEJ ANKETA KOL1 KOL2 KOL3 KOL USMENI UKUPNO OCJENA
1 Luciano Banjavčić RED 9.25 18.27 6.0 1 17.58 17.80 12.92 48.30 Zadovoljio/la 82.82 5
2 Filip Novak RED 10.00 11.10 9.0 NA 19.33 19.15 13.50 51.98 Zadovoljio/la 82.08 5
3 Andrija Štimac RED 9.88 12.83 10.0 NA 18.08 19.00 11.50 48.58 Zadovoljio/la 81.29 5
4 Marko Mencl RED 9.56 13.45 9.5 1 15.01 17.53 12.58 45.12 Zadovoljio/la 78.63 4
5 Marija Grozdek RED 9.30 15.04 6.0 1 17.43 15.35 13.58 46.37 Zadovoljio/la 77.70 4
6 Lucija Polak RED 9.68 13.76 8.0 NA 16.30 13.01 15.33 44.65 Zadovoljio/la 76.08 4
7 Luka Baksa RED 9.37 13.24 9.0 1 15.20 12.43 14.08 41.71 Zadovoljio/la 74.32 3
8 Nika Antolić RED 8.77 13.70 7.0 1 13.52 16.15 13.71 43.38 Zadovoljio/la 73.85 3
9 Paula Pažin RED 9.37 9.94 6.5 NA 16.50 16.45 15.05 48.00 Zadovoljio/la 73.81 3
10 Paula Zadravec RED 8.94 14.01 7.0 1 15.33 15.44 10.42 41.19 Zadovoljio/la 72.14 3
11 Ivana Hranj RED 9.88 12.32 9.5 NA 17.67 13.45 9.29 40.41 Zadovoljio/la 72.11 3
12 Anamarija Dominiković RED 9.50 13.80 6.0 1 13.07 13.76 13.42 40.25 Zadovoljio/la 70.55 3
13 Lovro Balent RED 8.52 13.18 7.0 1 14.20 15.78 10.42 40.40 Zadovoljio/la 70.10 3
14 Vlaho Srijense RED 9.36 10.00 9.0 1 14.68 15.34 9.62 39.64 Zadovoljio/la 69.00 3
15 Katarina Uremović RED 9.33 10.03 10.0 1 14.44 12.74 10.73 37.91 Zadovoljio/la 68.27 3
16 Marin Grabovac RED 9.88 10.80 6.5 NA 15.52 15.05 10.46 41.03 Zadovoljio/la 68.21 3
17 Benjamin Grgurević RED 9.12 9.11 7.0 1 11.80 16.25 13.33 41.38 Zadovoljio/la 67.61 3
18 Tomislav Ćosić RED 9.03 9.77 8.5 1 16.83 12.76 9.51 39.10 Zadovoljio/la 67.40 3
19 Luka Kukec RED 9.33 11.82 7.0 NA 18.33 11.05 9.30 38.68 Zadovoljio/la 66.83 3
20 Nensi Vugrinec RED 9.77 12.71 9.5 NA 14.55 11.05 8.56 34.16 Zadovoljio/la 66.14 3
21 Niko Rastija RED 9.88 8.61 10.0 1 15.83 12.02 8.64 36.49 Zadovoljio/la 65.98 3
22 Lara Budiša RED 9.51 9.03 6.5 1 15.95 12.41 11.50 39.86 Zadovoljio/la 65.90 3
23 Antonio Vinković RED 8.68 10.61 7.0 NA 15.93 15.60 7.96 39.48 Zadovoljio/la 65.78 3
24 Tin Muhar RED 7.80 11.88 5.5 1 16.22 11.60 11.71 39.53 Zadovoljio/la 65.71 3
25 Petar Svenšek RED 8.52 11.02 10.0 1 14.00 14.55 6.37 34.92 Zadovoljio/la 65.46 3
26 Sven Pavlić RED 9.51 8.74 7.5 NA 16.10 13.81 9.50 39.41 Zadovoljio/la 65.16 3
27 Marko Sokser RED 8.35 10.84 10.0 1 15.16 13.14 6.58 34.88 Zadovoljio/la 65.07 2
28 Dario Vučina RED 8.69 7.30 7.0 NA 16.58 13.99 11.39 41.96 Zadovoljio/la 64.95 3
29 Luka Šulentić RED 8.60 9.17 8.5 1 15.70 12.87 8.89 37.45 Zadovoljio/la 64.73 3
30 Filip Svetlečić RED 9.08 8.88 9.5 NA 14.77 13.26 7.81 35.84 Zadovoljio/la 63.30 3
31 Dorian Karnik RED 7.79 12.03 6.0 NA 15.00 14.69 7.40 37.10 Nije zadovoljio/la 62.91 1
32 Azra Gašparić RED 8.22 11.71 6.0 NA 11.69 12.19 12.96 36.84 Zadovoljio/la 62.77 2
33 Martina Vincijanović RED 8.16 8.38 7.5 1 18.67 12.35 6.57 37.59 Zadovoljio/la 62.63 3
34 Jan Posavec RED 8.64 9.03 9.5 1 17.08 6.75 10.18 34.01 Zadovoljio/la 62.18 3
35 David Brckan RED 7.35 11.12 9.0 1 12.78 10.91 9.57 33.25 Zadovoljio/la 61.73 3
36 Karlo Košulj RED 8.31 8.56 4.5 1 15.83 12.54 10.48 38.85 Zadovoljio/la 61.22 3
37 Petra Skoko RED 9.55 8.79 8.5 1 14.08 7.07 12.19 33.34 Zadovoljio/la 61.18 3
38 Jan Pobi RED 9.21 9.54 9.5 1 10.67 12.05 9.12 31.85 Zadovoljio/la 61.09 3
39 Tin Barbarić RED 7.90 11.06 3.5 NA 15.78 14.51 7.21 37.50 Zadovoljio/la 59.96 2
40 Antonio Martinaga RED 9.45 9.52 7.0 1 11.27 12.07 9.57 32.91 Zadovoljio/la 59.88 2
41 Mirta Vuković RED 7.06 10.35 8.0 NA 14.63 9.97 9.61 34.21 Zadovoljio/la 59.62 2
42 Lucia Andročec RED 9.11 10.68 5.0 1 14.08 7.62 12.09 33.79 Zadovoljio/la 59.58 2
43 Matej Ćutuk RED 9.61 11.13 6.0 NA 11.33 11.39 10.07 32.78 Zadovoljio/la 59.53 2
44 Jakov Malović RED 8.18 9.27 4.5 1 12.97 10.96 12.47 36.39 Zadovoljio/la 59.35 2
45 Filip Husnjak RED 7.08 11.28 9.5 1 12.35 12.33 5.75 30.43 Nije zadovoljio/la 59.29 1
46 Franjo Čotić RED 5.25 4.00 5.0 1 16.10 14.67 13.08 43.85 Nije zadovoljio/la 59.10 1
47 Niko Ivančić RED 4.91 10.63 6.0 NA 16.67 11.42 9.46 37.55 Zadovoljio/la 59.09 2
48 Vedran Bogdanović RED 8.47 11.97 6.5 1 11.85 11.53 7.58 30.96 Nije zadovoljio/la 58.90 1
49 Luka Šuto RED 8.70 8.49 9.5 NA 14.77 8.79 8.07 31.62 Zadovoljio/la 58.32 2
50 Jakov Zoričić RED 8.03 6.86 6.5 NA 13.22 7.90 15.69 36.81 Zadovoljio/la 58.20 2
51 Vid Matanić RED 7.64 8.71 9.5 1 10.40 12.25 8.54 31.19 Zadovoljio/la 58.04 2
52 Rafael Bistričić RED 7.65 11.70 6.5 1 12.67 9.09 9.42 31.17 Nije zadovoljio/la 58.03 1
53 Larija Jukić RED 7.43 9.52 9.5 1 14.09 8.60 7.78 30.47 Nije zadovoljio/la 57.92 1
54 Adrian Stojić RED 5.32 10.72 4.0 1 13.86 12.77 10.17 36.79 Zadovoljio/la 57.84 2
55 Nikola Polonijo RED 7.80 9.53 5.0 NA 16.00 11.84 7.61 35.45 Zadovoljio/la 57.78 2
56 Filip Markić RED 6.49 8.45 8.0 1 16.51 8.29 9.00 33.80 Zadovoljio/la 57.74 2
57 Domagoj Lovošević RED 9.55 9.62 6.0 1 11.97 13.01 6.57 31.56 Zadovoljio/la 57.72 2
58 Ennio David Komljenović RED 8.24 12.33 6.5 1 14.60 6.44 8.29 29.32 Nije zadovoljio/la 57.40 1
59 Matej Starčević RED 9.15 7.60 7.0 NA 16.93 10.94 5.58 33.45 Zadovoljio/la 57.20 2
60 Laura Benić RED 8.83 8.57 6.5 1 15.76 6.19 10.26 32.20 Zadovoljio/la 57.11 2
61 Marin Vabec RED 5.50 9.03 8.5 NA 11.87 12.35 9.84 34.06 Zadovoljio/la 57.09 2
62 Matej Šanko RED 9.67 7.65 10.0 1 10.02 7.70 10.99 28.70 Zadovoljio/la 57.03 2
63 Anja Svetličić RED 9.12 11.75 7.0 NA 10.72 7.35 10.93 29.00 Zadovoljio/la 56.87 2
64 David Sedlan RED 7.64 3.59 9.0 NA 9.27 9.62 17.75 36.64 Nije zadovoljio/la 56.87 1
65 Luka Hrupec RED 7.46 10.63 9.0 1 14.00 7.97 6.00 27.97 Zadovoljio/la 56.06 2
66 Mateo Čuvalo RED 6.64 8.16 5.0 1 13.80 12.84 7.69 34.33 Zadovoljio/la 55.13 2
67 Martin Trtanj RED 8.56 4.96 8.5 1 14.08 9.24 8.60 31.92 Zadovoljio/la 54.94 2
68 Veronika Dumić RED 8.94 9.01 6.5 1 12.42 7.90 9.05 29.37 Zadovoljio/la 54.82 2
69 Lana Savić RED 8.25 6.72 8.0 1 14.87 10.90 5.01 30.78 Zadovoljio/la 54.75 2
70 Nikola Bilić RED 6.81 11.94 6.5 1 12.83 8.05 7.58 28.47 Zadovoljio/la 54.71 2
71 Antonio Dekanić RED 6.64 9.20 5.0 1 13.75 10.03 8.92 32.69 Nije zadovoljio/la 54.54 1
72 Martin Premar RED 8.84 5.19 9.0 1 12.33 9.44 8.50 30.28 Zadovoljio/la 54.30 2
73 Karlo Vlah RED 8.05 8.76 6.0 NA 14.47 9.09 7.86 31.42 Zadovoljio/la 54.23 2
74 Marko Stipić RED 9.04 3.95 NA 1 15.50 14.94 9.57 40.01 Zadovoljio/la 54.00 2
75 Antonio Bobek RED 7.18 4.86 6.5 1 13.02 11.65 9.75 34.42 Zadovoljio/la 53.96 2
76 Marko Kovačić RED 7.88 8.76 6.5 1 12.95 8.40 8.36 29.71 Zadovoljio/la 53.85 2
77 Ivan Kraker RED 8.11 7.35 6.5 1 9.13 12.58 9.06 30.77 Zadovoljio/la 53.73 2
78 Ada Tikvan RED 8.45 7.66 7.0 1 14.42 8.82 6.38 29.62 Zadovoljio/la 53.73 2
79 Leonardo Mihalina RED 5.45 9.91 9.0 1 14.04 8.90 5.25 28.19 Nije zadovoljio/la 53.55 1
80 Roberto Šandro RED 7.99 5.42 7.5 1 12.17 10.46 8.99 31.61 Zadovoljio/la 53.53 2
81 Nikola Lazar RED 9.02 9.33 6.5 1 13.20 9.41 5.04 27.65 Zadovoljio/la 53.50 2
82 Maksim Kos RED 8.56 7.88 8.0 1 11.94 9.89 6.18 28.00 Zadovoljio/la 53.45 2
83 Jan Buruš RED 6.58 8.80 6.5 1 9.70 9.22 11.61 30.52 Nije zadovoljio/la 53.41 1
84 Val Bekić RED 5.63 8.33 7.5 1 13.01 11.09 6.58 30.69 Nije zadovoljio/la 53.14 1
85 Ivan Cimerman RED 8.02 9.08 7.0 NA 14.04 10.67 4.25 28.96 Nije zadovoljio/la 53.06 1
86 Leonardo Petran RED 7.94 6.83 7.0 1 10.17 9.37 10.74 30.28 Zadovoljio/la 53.05 2
87 Dorian Brezovec RED 7.64 5.85 6.0 1 12.05 10.83 9.65 32.53 Zadovoljio/la 53.02 2
88 Teo Galović RED 8.17 11.14 4.5 1 9.82 7.19 11.06 28.06 Nije zadovoljio/la 52.88 1
89 Lovro Špiljak RED 9.55 6.92 8.5 1 13.02 8.50 5.35 26.86 Nije zadovoljio/la 52.84 1
90 Boris Šarić RED 8.70 7.65 8.0 1 11.03 10.05 6.29 27.37 Nije zadovoljio/la 52.72 1
91 Lana Ljubičić RED 9.12 5.35 7.5 1 7.92 10.89 10.83 29.64 Zadovoljio/la 52.61 2
92 Šime Jović RED 7.51 9.10 NA 1 13.86 12.93 8.07 34.86 Zadovoljio/la 52.47 2
93 Mihael Novoselec RED 7.55 7.26 8.0 1 12.42 10.95 5.25 28.62 Zadovoljio/la 52.43 2
94 Nikola Brezovec RED 7.40 10.30 6.5 1 9.93 9.85 7.42 27.20 Nije zadovoljio/la 52.40 1
95 Andrej Pavešić RED 7.28 10.58 6.5 1 11.50 10.92 4.50 26.92 Zadovoljio/la 52.28 2
96 Viktoria Moguš RED 7.35 8.18 9.5 1 11.77 11.22 3.24 26.23 Nije zadovoljio/la 52.26 1
97 Jakov Štefanko RED 7.57 12.25 6.5 NA 12.40 9.70 3.67 25.77 Nije zadovoljio/la 52.09 1
98 Sara Mršić RED 8.78 5.60 7.5 1 8.00 9.45 11.75 29.20 Zadovoljio/la 52.08 2
99 Josip Abramović RED 9.02 11.28 4.0 1 9.68 7.90 9.17 26.76 Zadovoljio/la 52.05 2
100 Ema Andjelković RED 7.46 5.72 7.5 1 15.14 7.49 7.60 30.22 Nije zadovoljio/la 51.91 1
101 Roko Vlahov RED 8.19 7.39 5.0 1 12.72 6.62 10.85 30.19 Nije zadovoljio/la 51.77 1
102 Danijel Ciberlin Ivanić RED 8.47 7.78 7.0 1 13.25 5.63 8.55 27.43 Zadovoljio/la 51.68 2
103 Ivan Vlahović RED 8.46 2.99 4.0 1 12.35 11.43 11.35 35.13 Nije zadovoljio/la 51.58 1
104 Antonio Đimbrek RED 6.95 12.10 6.0 1 9.03 10.51 5.95 25.49 Zadovoljio/la 51.54 2
105 Luka Mlinar RED 7.32 5.69 5.0 1 12.38 9.67 10.47 32.52 Nije zadovoljio/la 51.53 1
106 Dino Habijanac RED 8.13 6.65 6.5 NA 10.13 11.17 8.88 30.19 Nije zadovoljio/la 51.46 1
107 Fran Uljarević RED 8.35 8.63 7.0 1 11.92 7.27 7.21 26.41 Nije zadovoljio/la 51.38 1
108 Karlo Mišić RED 8.22 7.70 4.0 1 14.58 6.75 9.03 30.36 Nije zadovoljio/la 51.28 1
109 David Šimičević RED 3.90 9.68 7.0 1 11.10 11.21 7.17 29.48 Zadovoljio/la 51.06 2
110 Mia Šižgorić RED 6.93 4.73 6.5 1 9.00 5.55 17.33 31.88 Nije zadovoljio/la 51.04 1
111 Matko Grbić RED 1.74 10.81 5.5 1 11.33 11.18 9.42 31.93 Nije zadovoljio/la 50.98 1
112 Ivan Leško RED 8.84 4.59 6.5 1 13.56 10.15 6.29 29.99 Nije zadovoljio/la 50.93 1
113 Janko Antonina RED 6.40 7.01 6.0 NA 14.68 8.94 7.81 31.42 Nije zadovoljio/la 50.84 1
114 Mario Trošt RED 7.96 8.63 9.0 NA 8.52 8.20 8.43 25.16 Zadovoljio/la 50.74 2
115 Marko Žuraj RED 7.20 10.82 4.0 1 9.63 9.59 8.45 27.67 Nije zadovoljio/la 50.69 1
116 Franka Marciuš RED 7.81 5.49 7.5 1 8.73 9.29 10.86 28.88 Zadovoljio/la 50.68 2
117 Špiro Pravdić RED 7.96 4.53 10.0 NA 12.37 9.65 6.17 28.19 Zadovoljio/la 50.68 2
118 Juraj Vito Kuhtić RED 9.36 8.29 7.0 1 6.77 9.00 9.24 25.00 Nije zadovoljio/la 50.66 1
119 Filip Levis RED 5.87 7.58 4.5 NA 14.22 10.30 7.83 32.35 Nije zadovoljio/la 50.30 1
120 Lovro Sverić RED 8.35 9.14 5.0 1 9.83 7.65 9.25 26.73 Nije zadovoljio/la 50.22 1
121 Lovro Ivanković RED 7.84 3.44 8.5 NA 11.13 11.10 8.15 30.39 Zadovoljio/la 50.16 2
122 Sara Krišto RED 6.36 6.64 7.5 1 9.30 11.65 7.68 28.62 Zadovoljio/la 50.13 2
123 Andrea Klinac RED 7.38 9.05 6.0 NA 11.06 10.92 5.71 27.69 Nije zadovoljio/la 50.12 1
124 David Gelenčir RED 7.88 12.70 6.0 1 9.00 6.45 7.09 22.54 NA 50.12 1
125 Bojan Horvat RED 7.81 9.25 4.0 1 10.83 7.22 9.95 28.00 Zadovoljio/la 50.06 2
126 Jakov Marčan RED 6.42 7.67 5.5 1 13.05 9.43 6.96 29.45 Zadovoljio/la 50.03 2
127 Rea Marković RED 9.18 9.36 6.5 1 8.55 7.84 7.48 23.86 NA 49.91 1
128 Niko Korać RED 3.22 3.17 8.0 NA 12.63 11.91 10.01 34.55 NA 48.94 1
129 Mia Pahanić RED 8.10 3.95 8.0 1 14.77 8.13 4.99 27.89 NA 48.94 1
130 Marko Katalenić RED 7.74 9.79 6.5 1 11.67 6.89 5.29 23.85 NA 48.88 1
131 Josip Novak RED 7.71 7.15 4.0 1 13.60 7.69 7.71 28.99 NA 48.86 1
132 Jure Jelčić RED 6.83 8.55 2.5 1 11.60 10.15 8.10 29.85 NA 48.73 1
133 Mihael Lončarić RED 5.43 7.94 6.0 1 10.44 10.22 7.69 28.35 NA 48.72 1
134 Luka Primorac RED 7.72 10.30 4.0 1 9.67 9.52 6.43 25.62 NA 48.64 1
135 Petar Perenčević RED 7.95 3.86 2.0 1 11.02 11.42 11.35 33.80 NA 48.60 1
136 Luka Arbutina RED 5.88 10.13 8.5 1 8.30 7.54 7.05 22.89 NA 48.40 1
137 Lucijan Raspović RED 9.09 3.16 8.5 1 12.33 7.24 6.89 26.46 NA 48.21 1
138 Lovro Horvat RED 4.91 10.36 4.5 NA 10.78 9.38 8.21 28.38 NA 48.14 1
139 Lucija Luk RED 8.64 10.26 6.0 1 9.25 3.00 9.94 22.19 NA 48.09 1
140 Dejan Smoljo RED 7.98 8.43 4.5 NA 12.25 6.17 8.70 27.11 NA 48.03 1
141 Dino Limari RED 8.81 8.46 5.0 1 8.09 5.87 10.80 24.75 NA 48.03 1
142 Antonio Borjan RED 5.10 8.03 6.5 NA 8.26 9.90 10.17 28.33 NA 47.96 1
143 Jan Jedvaj RED 6.81 5.71 3.5 1 11.30 9.48 9.96 30.75 NA 47.76 1
144 Simone Jurcan RED 7.93 5.24 9.0 NA 11.08 8.21 6.28 25.57 NA 47.74 1
145 Josip Stojak RED 7.36 1.84 8.0 1 13.05 8.48 7.76 29.29 NA 47.49 1
146 Roberto Brekalo RED 6.80 5.72 9.0 1 13.58 7.89 3.15 24.62 NA 47.14 1
147 Denis Mihin RED 7.87 6.78 5.0 NA 11.27 9.44 6.71 27.41 NA 47.07 1
148 Augustin Moškun RED 4.84 8.05 7.5 NA 8.42 9.25 8.96 26.63 NA 47.02 1
149 Antonio Blaž RED 3.95 8.87 6.5 1 8.67 8.51 9.33 26.51 NA 46.83 1
150 Dominik Buri RED 8.17 8.55 6.5 NA 8.60 8.12 6.83 23.55 NA 46.77 1
151 Ivan Trošeljac RED 7.50 2.88 9.5 1 11.82 7.47 6.50 25.79 NA 46.67 1
152 Noa Holešek RED 5.53 4.83 4.0 1 10.60 12.98 7.54 31.12 NA 46.48 1
153 Toni Marić RED 7.79 6.37 7.0 NA 8.67 11.08 5.56 25.31 NA 46.47 1
154 Filip Vragotuk RED 7.04 4.90 8.5 1 8.62 9.16 7.25 25.03 NA 46.47 1
155 Ivan Grgos RED 3.89 4.85 5.5 1 12.03 11.34 7.79 31.17 NA 46.40 1
156 Borna Petrović RED 8.38 8.07 6.0 1 10.25 5.75 6.95 22.95 NA 46.40 1
157 Matija Volarić RED 9.04 7.70 7.0 1 5.46 6.83 9.37 21.66 NA 46.40 1
158 Leonardo Andrašić RED 5.20 5.18 4.0 NA 11.50 10.20 10.08 31.79 NA 46.16 1
159 Dino Plodinec RED 8.15 7.96 6.0 NA 9.87 5.02 8.99 23.87 NA 45.99 1
160 Nika Laštro RED 6.58 4.06 7.5 1 10.15 9.30 7.31 26.76 NA 45.90 1
161 Lovro Vidović RED 4.80 9.52 4.5 NA 10.63 10.38 5.96 26.97 NA 45.79 1
162 Lovro Đurđević RED 6.24 5.75 4.0 NA 9.80 10.05 9.80 29.65 NA 45.64 1
163 Mislav Mlinarić RED 5.53 4.51 5.5 1 10.58 9.65 8.87 29.11 NA 45.64 1
164 Mateo Strinavić RED 7.14 4.75 8.5 1 7.19 10.20 5.83 23.22 NA 44.61 1
165 Ante Petrović RED 6.60 5.04 6.0 NA 11.33 9.08 6.45 26.87 NA 44.50 1
166 Jakov Ferko RED 7.82 9.59 5.5 NA 10.54 6.26 4.75 21.54 NA 44.46 1
167 Dora Kulaš RED 7.36 6.29 4.5 NA 13.83 7.80 4.49 26.13 NA 44.27 1
168 Matija Sarić RED 5.18 4.89 5.0 1 10.51 10.35 6.77 27.64 NA 43.70 1
169 Lea Maria Dobrić RED 6.91 6.27 5.0 1 10.69 7.42 6.12 24.23 NA 43.41 1
170 Anid Selimi RED 4.05 8.85 3.5 NA 15.60 3.40 7.84 26.84 NA 43.24 1
171 Daniel Mišić RED 7.15 5.89 7.0 1 9.27 5.57 7.08 21.92 NA 42.96 1
172 David Čuturić RED 7.89 5.95 6.5 1 10.65 6.90 4.04 21.59 NA 42.93 1
173 Danijela Stjepanović RED 7.12 6.43 7.5 1 8.83 7.77 4.21 20.81 NA 42.86 1
174 Lovro Rozman RED 8.29 7.07 NA NA 14.33 13.16 NA 27.49 NA 42.85 1
175 Ivan Šango RED 7.95 5.41 4.5 1 10.83 7.13 5.98 23.94 NA 42.80 1
176 Nikola Mirilović RED 4.83 5.78 5.0 1 8.58 9.60 7.86 26.04 NA 42.65 1
177 Marin Šalaj RED 5.64 3.70 6.0 1 13.45 9.10 3.49 26.04 NA 42.38 1
178 Filip Bogomolec RED 6.53 8.12 4.0 1 8.43 7.55 6.12 22.10 NA 41.75 1
179 Tomislav Hunjadi-Šelendić RED 7.17 6.99 3.5 1 9.10 8.50 5.46 23.06 NA 41.72 1
180 Matej Vidović RED 5.74 5.21 6.5 1 14.07 5.85 3.29 23.21 NA 41.66 1
181 Kristijan Blagus RED 4.25 7.42 5.0 NA 11.74 6.79 6.07 24.60 NA 41.27 1
182 Dino Zerec RED 6.26 9.35 5.0 NA 9.04 5.88 5.33 20.25 NA 40.86 1
183 Bartol Kolaković RED 5.27 7.23 2.5 1 11.30 8.60 3.17 23.07 NA 39.07 1
184 Hrvoje Vašarević RED 5.37 4.35 5.5 1 9.50 9.52 3.67 22.69 NA 38.91 1
185 Patrik Klarić RED 5.17 7.83 3.0 NA 9.05 10.45 2.67 22.17 NA 38.17 1
186 Matej Jurić RED 5.09 5.53 5.0 1 7.43 10.60 3.29 21.32 NA 37.94 1
187 Hrvoje Mišanec RED 7.84 7.04 3.0 NA 10.17 7.09 2.25 19.50 NA 37.39 1
188 Antonijo Adžaga RED 5.21 8.64 5.5 NA 10.33 6.90 NA 17.23 NA 36.58 1
189 Gabriel Kruljac RED 5.07 4.83 4.0 NA 11.23 6.98 3.70 21.92 NA 35.81 1
190 Josipa Jurić RED 5.30 5.57 6.0 NA 11.18 6.13 1.33 18.65 NA 35.51 1
191 Nikola Mačukatić RED 6.06 6.61 6.0 1 9.51 6.13 NA 15.64 NA 35.31 1
192 Benedikt Buhin RED 2.67 6.83 5.0 NA 9.62 7.67 3.42 20.70 NA 35.21 1
193 Leon Dreven RED 5.19 7.42 5.5 1 8.87 2.52 4.71 16.09 NA 35.21 1
194 Ivan Boščić RED 6.66 7.21 NA 1 8.00 11.10 1.00 20.10 NA 34.97 1
195 Marija Magdalena Ilić RED 7.79 5.36 7.5 1 9.02 4.30 NA 13.32 NA 34.97 1
196 Josip Lačić RED 6.28 5.01 6.0 1 11.25 5.27 0.00 16.52 NA 34.81 1
197 Patrik Kosor RED 6.54 5.92 NA NA 8.04 8.79 5.49 22.32 NA 34.78 1
198 Tin Podnar RED 7.86 6.09 5.0 1 5.59 4.54 4.29 14.42 NA 34.37 1
199 Antonio Muža RED 7.68 3.11 3.0 NA 8.75 5.72 6.01 20.48 NA 34.27 1
200 Fran Čiković RED 3.22 4.81 4.0 1 12.55 5.82 2.84 21.21 NA 34.24 1
201 Marko Miličević RED 3.75 8.81 NA NA 12.00 9.57 NA 21.57 NA 34.13 1
202 Bartol Matolić RED 6.15 5.77 4.5 NA 6.83 9.97 0.00 16.80 NA 33.22 1
203 Luka Prelog RED 1.96 6.77 6.0 1 8.52 5.57 3.33 17.42 NA 33.15 1
204 Luka Knezović RED 5.34 5.89 6.5 1 7.75 6.35 NA 14.10 NA 32.83 1
205 Lana Penić RED 4.39 5.40 3.5 NA 7.90 5.74 5.79 19.43 NA 32.72 1
206 Barbara Cvetan RED 9.25 4.46 NA NA 6.07 7.14 5.46 18.66 NA 32.38 1
207 Nino Kukec RED 2.77 4.88 NA NA 12.45 5.15 7.07 24.67 NA 32.32 1
208 Klara Tepić RED 8.77 4.13 5.5 1 6.50 6.33 NA 12.83 NA 32.23 1
209 Ana Jurišić RED 7.23 5.65 NA NA 9.22 8.55 1.50 19.27 NA 32.15 1
210 Andrija Miličević RED 3.68 6.70 6.5 NA 7.50 7.52 NA 15.02 NA 31.90 1
211 Gabriela Perković RED 7.02 2.07 5.0 1 10.54 1.74 4.46 16.73 NA 31.83 1
212 Vedran Solil RED 4.90 2.94 3.5 1 9.95 9.37 NA 19.32 NA 31.66 1
213 Ivan Petrinić RED 5.20 7.76 NA NA 9.75 5.60 2.96 18.31 NA 31.27 1
214 Domagoj Poje RED 0.00 5.62 6.0 1 12.03 6.20 NA 18.23 NA 30.85 1
215 Luka Cigić RED 4.32 7.43 5.5 1 7.75 3.51 1.33 12.59 NA 30.84 1
216 Lana Kelić RED 4.90 5.34 4.0 NA 7.83 5.03 3.54 16.40 NA 30.64 1
217 Marko Vukšić RED 4.36 1.99 5.0 NA 11.77 7.40 NA 19.17 NA 30.52 1
218 Marija Kelemen RED 3.57 5.92 6.0 NA 9.53 5.47 0.00 15.01 NA 30.49 1
219 Ema Stanković RED 7.50 3.83 3.0 1 3.97 2.62 8.45 15.03 NA 30.37 1
220 Luka Rakić RED 4.87 6.94 3.5 1 6.28 7.70 NA 13.98 NA 30.29 1
221 Atila Jozić RED 5.47 4.73 4.5 NA 8.96 3.59 3.00 15.54 NA 30.25 1
222 Ivan Bašljan RED 7.12 6.51 8.5 1 0.00 6.95 NA 6.95 NA 30.08 1
223 Patrik David Klanjčić RED 7.17 8.02 4.0 NA 6.89 3.61 NA 10.50 NA 29.69 1
224 Lara Miličević RED 5.60 4.21 NA NA 8.10 6.48 5.24 19.83 NA 29.63 1
225 Viktoria Vuljanić RED 6.20 3.63 6.0 1 6.00 5.65 1.00 12.65 NA 29.48 1
226 Paula Molnar RED 5.47 4.45 3.0 NA 5.00 6.63 4.46 16.09 NA 29.01 1
227 Karlo Kuljić RED 3.96 0.00 NA NA 5.68 10.64 8.53 24.85 NA 28.81 1
228 Franko Tomić RED 6.60 4.44 4.0 NA 8.17 3.60 1.79 13.55 NA 28.60 1
229 Juraj Kovačić RED 0.00 9.36 7.0 NA 7.99 4.19 NA 12.18 NA 28.54 1
230 Martin Bubnjarić RED 4.69 3.93 NA NA 10.56 3.57 5.54 19.67 NA 28.29 1
231 Lea Kosanović RED 4.53 3.34 NA NA 10.30 6.98 3.08 20.36 NA 28.23 1
232 Benjamin Hrman RED 2.77 2.58 5.5 NA 7.23 7.56 2.51 17.30 NA 28.15 1
233 Sebastian Hat RED 6.65 7.13 5.0 NA 3.58 5.68 NA 9.27 NA 28.04 1
234 Fran Derifaj RED 1.50 5.10 6.0 1 8.80 5.55 NA 14.35 NA 27.95 1
235 Luka Šolaja RED 4.23 3.86 6.0 NA 6.47 7.38 NA 13.85 NA 27.94 1
236 Matija Matić RED 2.63 5.80 NA NA 11.83 7.29 NA 19.12 NA 27.55 1
237 David Mikolaj RED 7.45 4.50 1.0 NA NA 5.55 8.08 13.63 NA 26.58 1
238 Emerik Vuk RED 6.00 5.10 NA NA 9.68 5.58 NA 15.27 NA 26.36 1
239 Patrik Ovad RED 4.44 3.73 NA NA 7.68 7.08 3.21 17.97 NA 26.14 1
240 Luka Ružić RED 4.66 6.78 4.0 1 6.10 3.52 NA 9.62 NA 26.06 1
241 Josip Rabadžija RED 3.08 1.90 NA NA 5.92 6.54 8.58 21.04 NA 26.02 1
242 Fran Lesjak RED 0.00 2.04 7.0 NA 10.85 5.98 NA 16.83 NA 25.87 1
243 Niko Šestan RED 7.32 4.46 NA NA 8.78 NA 5.17 13.95 NA 25.73 1
244 Luka Kačarik RED 3.22 3.37 3.5 NA 9.75 5.85 NA 15.60 NA 25.69 1
245 Filomena Javorčić RED 1.88 4.24 1.5 1 8.33 8.72 NA 17.05 NA 25.67 1
246 Paula Cesar RED 5.09 5.87 NA NA 7.21 4.23 3.01 14.45 NA 25.41 1
247 Filip Lukić RED 6.43 6.77 NA NA 7.50 4.71 NA 12.21 NA 25.41 1
248 Martin Šiljeg RED 4.06 3.71 NA NA 11.25 6.37 NA 17.62 NA 25.39 1
249 Gabriel Trajbar RED 1.68 4.52 NA NA 11.81 7.07 NA 18.88 NA 25.08 1
250 Luka Horvat RED 2.10 1.71 3.0 NA 7.07 6.07 4.89 18.02 NA 24.84 1
251 Fran Martinović RED 4.74 5.45 NA NA 8.36 0.00 5.79 14.15 NA 24.34 1
252 Valentina Kirin RED 5.00 0.57 NA NA 11.20 4.15 3.33 18.68 NA 24.25 1
253 Paola Čabraja RED 3.75 7.27 NA NA 5.23 6.56 1.41 13.20 NA 24.22 1
254 Juraj Tomiša RED 5.65 4.57 1.0 1 4.76 7.08 NA 11.84 NA 24.06 1
255 Mateo Blažek RED 5.42 4.33 NA NA 8.18 2.78 3.33 14.29 NA 24.04 1
256 Martin Šek RED 6.88 2.05 NA 1 7.54 6.35 NA 13.89 NA 23.82 1
257 Mark Cvitan RED 4.58 3.94 NA NA 11.06 4.17 NA 15.23 NA 23.75 1
258 Matej Mavrek RED 2.88 8.12 NA NA 4.98 7.62 NA 12.61 NA 23.60 1
259 Sven Dominik Srbljinović RED 3.06 0.50 NA NA 9.08 7.82 3.12 20.02 NA 23.58 1
260 Vito Novak RED 3.15 3.56 NA 1 5.67 4.78 5.08 15.53 NA 23.24 1
261 Jura Bošnjak RED 5.24 2.52 6.5 NA 5.67 3.24 NA 8.90 NA 23.17 1
262 David Lazar RED 6.55 5.57 NA NA 8.67 NA 2.34 11.01 NA 23.13 1
263 Nikolina Šuto RED 7.73 3.40 NA NA 6.08 5.90 0.00 11.98 NA 23.11 1
264 Ante Čerkez RED 4.72 3.52 NA NA 8.17 NA 6.50 14.67 NA 22.91 1
265 Mateo Šerer RED 1.88 4.72 NA NA 7.98 8.09 NA 16.07 NA 22.67 1
266 Patrik Lacić RED 5.95 1.54 NA NA NA 7.21 7.87 15.08 NA 22.57 1
267 Lovro Rendulić RED 6.90 4.19 NA NA 7.83 3.57 NA 11.40 NA 22.49 1
268 Tomas Slavulj RED 2.42 2.57 NA NA 4.27 6.87 6.23 17.37 NA 22.36 1
269 Leo Vrhovec RED 0.00 6.38 NA NA 4.44 4.20 7.29 15.93 NA 22.31 1
270 Marko Bosanac RED 5.06 6.77 NA NA 6.17 4.23 0.00 10.40 NA 22.23 1
271 Lovro Počepan RED 4.83 1.20 NA NA 7.94 8.25 NA 16.19 NA 22.22 1
272 Bruno Rašić RED 5.32 2.65 NA NA 11.17 2.98 NA 14.15 NA 22.12 1
273 Karlo Tukša RED 4.27 3.23 NA NA 9.67 4.91 NA 14.58 NA 22.08 1
274 Karlo Lonjak RED 6.69 4.73 NA NA 7.82 2.76 NA 10.57 NA 22.00 1
275 Saša Kordić RED 4.00 0.00 3.5 1 NA 5.70 7.63 13.33 NA 21.83 1
276 Josip Kokolek RED 1.98 6.56 NA NA 6.52 6.65 NA 13.17 NA 21.71 1
277 Filip Valentić RED 5.56 2.83 4.5 1 6.77 0.87 NA 7.63 NA 21.53 1
278 Vladan Krivokapić RED 4.74 4.56 NA NA 5.87 0.00 5.95 11.81 NA 21.12 1
279 Stjepan Lovrić RED 3.43 2.13 4.0 1 3.96 3.15 3.37 10.47 NA 21.04 1
280 Maja Just RED 0.00 1.00 4.5 NA 6.00 3.90 4.99 14.89 NA 20.39 1
281 Toni Tojčić RED 7.82 5.10 NA NA 3.68 3.69 0.00 7.37 NA 20.29 1
282 Gabriel Šepak RED 2.80 4.81 NA NA 4.88 3.38 4.33 12.60 NA 20.20 1
283 Gabriel Šuput RED 6.22 2.90 NA NA 6.00 5.00 NA 11.00 NA 20.12 1
284 Tomas Carlin RED 2.30 4.61 NA NA 6.76 6.41 NA 13.17 NA 20.08 1
285 Ivan Markovinović RED 3.99 3.75 NA 1 6.60 2.85 1.33 10.78 NA 19.52 1
286 Leon Čižmarević RED 6.05 1.42 4.0 NA 5.44 0.70 1.75 7.89 NA 19.36 1
287 Roko Trošić RED 0.50 3.53 NA NA 6.89 4.45 3.87 15.21 NA 19.24 1
288 Sandro Celovec RED 4.15 1.34 NA NA 7.47 6.05 NA 13.52 NA 19.01 1
289 Filip Matanić RED 4.55 1.59 1.0 NA 2.92 4.07 4.87 11.85 NA 19.00 1
290 Luka Potočki RED 2.40 1.10 3.5 1 2.69 NA 8.23 10.92 NA 18.92 1
291 Luka Grudenić RED 5.00 3.84 NA NA 10.00 NA NA 10.00 NA 18.84 1
292 Luka Kerovec RED 2.12 0.17 3.5 NA 7.10 4.77 NA 11.87 NA 17.66 0
293 Niko Ivanić RED 5.28 5.88 6.5 NA 0.00 NA NA 0.00 NA 17.66 1
294 Matej Božinović-Karauz RED 3.87 3.43 NA NA 10.03 NA NA 10.03 NA 17.33 0
295 Ivo Šutić RED 2.53 1.68 NA NA 4.96 8.03 NA 12.99 NA 17.20 0
296 Leo Simon RED 0.00 4.95 NA NA 7.36 4.60 NA 11.96 NA 16.91 0
297 Petar Znika RED 1.49 3.35 NA NA 5.93 6.10 NA 12.03 NA 16.87 0
298 David Gudan RED 1.50 1.79 NA NA 8.23 4.65 NA 12.87 NA 16.17 0
299 Filip Jamuljak RED 0.00 1.34 NA NA 7.04 7.75 NA 14.79 NA 16.13 0
300 Luka Pavić RED 1.96 0.78 NA NA 13.17 NA NA 13.17 NA 15.91 0
301 Rene Crnković RED 3.49 3.25 4.0 NA 4.92 NA NA 4.92 NA 15.66 1
302 Luka Peršić RED 3.10 3.11 NA NA 9.42 NA NA 9.42 NA 15.63 0
303 Marko Fundak RED 3.78 0.26 NA NA 9.87 NA NA 9.87 NA 13.91 1
304 Tea Cvitić RED 3.63 3.78 NA NA 6.28 NA NA 6.28 NA 13.69 0
305 Marko Malenica RED 0.00 1.66 3.0 NA 4.32 3.48 NA 7.81 NA 12.46 0
306 Jan Puzak RED 2.50 1.50 NA NA 5.03 2.56 NA 7.59 NA 11.59 0
307 Eva Špoljarić RED 0.00 0.00 6.0 NA NA 4.77 NA 4.77 NA 10.77 0
308 Mihael Cik RED 0.00 0.03 4.0 NA 4.75 1.70 NA 6.45 NA 10.48 0
309 Tomislav Alerić RED 0.00 1.08 NA NA 8.98 NA NA 8.98 NA 10.06 0
310 Krešimir Pavić RED 0.00 0.79 NA NA 6.69 2.55 NA 9.24 NA 10.03 0
311 Bruno Čović RED 4.55 2.08 NA NA 0.00 NA NA 0.00 NA 6.63 -1
312 Ivan Repušić RED 0.00 1.18 NA NA 3.92 NA NA 3.92 NA 5.10 0
313 Ivan Rocek RED 0.00 0.00 NA NA 3.08 NA NA 3.08 NA 3.08 0
314 Leon Marić RED 0.00 2.79 NA NA NA NA NA NA NA 2.79 0
315 Laura Kronast RED 0.00 1.10 NA NA NA NA NA NA NA 1.10 -1
316 Stjepan Sačer RED 0.00 1.00 NA NA NA NA NA NA NA 1.00 0
317 Leon Rabuzin RED 0.00 0.90 NA NA NA NA NA NA NA 0.90 0
318 Domagoj Bebić RED 0.00 0.00 NA NA NA NA NA NA NA 0.00 0
319 Filip Bošković RED 0.00 0.00 NA NA NA NA NA NA NA 0.00 0
320 Marin Cvetko RED 0.00 0.00 NA NA NA NA NA NA NA 0.00 0
321 Dario Dmejhal RED 0.00 0.00 NA NA NA NA NA NA NA 0.00 0
322 Dino Galović RED 0.00 NA NA NA NA NA NA NA NA 0.00 0
323 Niko Gracin RED 0.00 NA NA NA NA NA NA NA NA 0.00 0
324 Viktor Hip RED 0.00 0.00 NA NA NA NA NA NA NA 0.00 0
325 Katarina Hozjan RED 0.00 0.00 NA NA NA NA NA NA NA 0.00 -1
326 Mihael Jakuš RED 0.00 0.00 NA NA NA NA NA NA NA 0.00 0
327 Martin Kelemen RED 0.00 0.00 NA NA NA NA NA NA NA 0.00 -1
328 Krešimir Krunić RED 0.00 0.00 NA NA NA NA NA NA NA 0.00 0
329 Danylo Kurochkin RED 0.00 NA NA NA NA NA NA NA NA 0.00 -1
330 Borna Lončarević RED 0.00 0.00 NA NA NA NA NA NA NA 0.00 0
331 Celestin Mihelj RED 0.00 0.00 NA NA NA NA NA NA NA 0.00 0
332 Tin Mlinarić RED 0.00 0.00 NA NA NA NA NA NA NA 0.00 0
333 Teo Pačkovski RED 0.00 NA NA NA NA NA NA NA NA 0.00 0
334 Ivan Slišković RED 0.00 0.00 NA NA NA NA NA NA NA 0.00 0
335 Andriy Snigur RED 0.00 0.00 NA NA NA NA NA NA NA 0.00 0
336 Dominik Toplek RED 0.00 0.00 NA NA NA NA NA NA NA 0.00 0
337 Lukas Vounasis RED 0.00 0.00 NA NA NA NA NA NA NA 0.00 0
338 Jan Čovčić RED 0.00 NA NA NA NA NA NA NA NA 0.00 0
339 Antonio Đopar RED 0.00 NA NA NA NA NA NA NA NA 0.00 0
340 Zlatko Pračić RAZ 0.00 0.75 10.0 1 13.60 14.66 6.17 34.43 Zadovoljio/la 45.43 3
341 Mia Peharec RAZ 1.23 6.64 5.0 1 13.28 9.22 9.58 32.08 Zadovoljio/la 38.08 3
342 Emilia Jelačić RAZ 4.93 8.63 10.0 NA 14.55 5.91 7.06 27.52 Zadovoljio/la 37.52 3
343 Mateo Besednik RAZ 2.34 4.49 NA NA 9.91 8.65 8.75 27.31 NA 27.31 0
344 Jakov Knežević RAZ 0.00 NA NA NA 6.42 2.77 NA 9.19 NA 9.19 0
345 Dora Žegerc RAZ 0.00 0.00 NA NA 2.58 NA NA 2.58 NA 2.58 0
346 Zlatko Rajković RAZ 0.17 0.50 NA NA 1.84 NA NA 1.84 NA 1.84 0
347 Borna Alvir RAZ 0.00 0.00 NA NA NA NA NA NA NA 0.00 0
348 Helena Babić RAZ 0.00 0.00 NA NA NA NA NA NA NA 0.00 0
349 Dora Fabijanić RAZ 0.00 NA NA NA NA NA NA NA NA 0.00 0
350 Luka Gvozdanović RAZ 0.00 NA NA NA NA NA NA NA NA 0.00 0
351 Mirjana Matučec RAZ 0.00 NA NA NA NA NA NA NA NA 0.00 0
352 Antonio Pavlović RAZ 0.00 NA NA NA NA NA NA NA NA 0.00 0
353 Vinka Protega RAZ 0.00 NA NA NA NA NA NA NA NA 0.00 0
354 Monika Žunec RAZ 0.00 0.00 NA NA NA NA NA NA NA 0.00 0
355 Marin Žužić RAZ 0.00 NA NA NA NA NA NA NA NA 0.00 0
356 Matteo Lež IZV 0.00 0.00 8.0 NA 13.95 17.96 4.59 36.50 Zadovoljio/la 44.50 3
357 Dino Pizek IZV 0.00 NA 7.5 NA 13.42 13.80 5.49 32.72 Zadovoljio/la 40.21 2
358 Neo Mareković IZV 4.06 7.75 5.0 1 8.05 8.99 6.92 23.95 NA 29.96 1
359 Natalija Kokanović IZV 0.00 0.00 5.5 NA 12.51 5.35 0.00 17.86 NA 23.36 1
360 Bruno Čavarović IZV 0.00 0.00 7.0 NA 5.57 5.00 0.00 10.57 NA 17.57 1
361 Filip Fric IZV 0.00 0.00 4.0 NA NA NA NA NA NA 4.00 1
362 Šimun Mudronja IZV 3.23 1.84 NA NA 0.00 NA NA 0.00 NA 0.00 0
363 Borna Belić IZV 4.31 1.46 NA NA NA NA NA NA NA 0.00 1
364 Frana Bolješić IZV 0.00 0.00 NA NA NA NA NA NA NA 0.00 0
365 Vladimir Dujnić IZV 0.00 NA NA NA NA NA NA NA NA 0.00 0
366 Jan Gošić IZV 0.00 0.00 NA NA NA NA NA NA NA 0.00 0
367 Frano Primorac IZV 0.00 0.00 NA NA NA NA NA NA NA 0.00 0
368 Sven Sesvečan IZV 0.00 0.00 NA NA NA NA NA NA NA 0.00 0
369 Ana Marija Trogrlić IZV 0.00 0.00 NA NA NA NA NA NA NA 0.00 0

Kolokvirali

Studenti koji su kolokvirali
id Ime Prezime Status mail DZ KVIZ ESEJ ANKETA KOL1 KOL2 KOL3 KOL USMENI UKUPNO OCJENA
1 Luciano Banjavčić RED 9.25 18.27 6.0 1 17.58 17.80 12.92 48.30 Zadovoljio/la 82.82 5
2 Filip Novak RED 10.00 11.10 9.0 NA 19.33 19.15 13.50 51.98 Zadovoljio/la 82.08 5
3 Andrija Štimac RED 9.88 12.83 10.0 NA 18.08 19.00 11.50 48.58 Zadovoljio/la 81.29 5
4 Marko Mencl RED 9.56 13.45 9.5 1 15.01 17.53 12.58 45.12 Zadovoljio/la 78.63 4
5 Marija Grozdek RED 9.30 15.04 6.0 1 17.43 15.35 13.58 46.37 Zadovoljio/la 77.70 4
6 Lucija Polak RED 9.68 13.76 8.0 NA 16.30 13.01 15.33 44.65 Zadovoljio/la 76.08 4
7 Luka Baksa RED 9.37 13.24 9.0 1 15.20 12.43 14.08 41.71 Zadovoljio/la 74.32 3
8 Nika Antolić RED 8.77 13.70 7.0 1 13.52 16.15 13.71 43.38 Zadovoljio/la 73.85 3
9 Paula Pažin RED 9.37 9.94 6.5 NA 16.50 16.45 15.05 48.00 Zadovoljio/la 73.81 3
10 Paula Zadravec RED 8.94 14.01 7.0 1 15.33 15.44 10.42 41.19 Zadovoljio/la 72.14 3
11 Ivana Hranj RED 9.88 12.32 9.5 NA 17.67 13.45 9.29 40.41 Zadovoljio/la 72.11 3
12 Anamarija Dominiković RED 9.50 13.80 6.0 1 13.07 13.76 13.42 40.25 Zadovoljio/la 70.55 3
13 Lovro Balent RED 8.52 13.18 7.0 1 14.20 15.78 10.42 40.40 Zadovoljio/la 70.10 3
14 Vlaho Srijense RED 9.36 10.00 9.0 1 14.68 15.34 9.62 39.64 Zadovoljio/la 69.00 3
15 Katarina Uremović RED 9.33 10.03 10.0 1 14.44 12.74 10.73 37.91 Zadovoljio/la 68.27 3
16 Marin Grabovac RED 9.88 10.80 6.5 NA 15.52 15.05 10.46 41.03 Zadovoljio/la 68.21 3
17 Benjamin Grgurević RED 9.12 9.11 7.0 1 11.80 16.25 13.33 41.38 Zadovoljio/la 67.61 3
18 Tomislav Ćosić RED 9.03 9.77 8.5 1 16.83 12.76 9.51 39.10 Zadovoljio/la 67.40 3
19 Luka Kukec RED 9.33 11.82 7.0 NA 18.33 11.05 9.30 38.68 Zadovoljio/la 66.83 3
20 Nensi Vugrinec RED 9.77 12.71 9.5 NA 14.55 11.05 8.56 34.16 Zadovoljio/la 66.14 3
21 Niko Rastija RED 9.88 8.61 10.0 1 15.83 12.02 8.64 36.49 Zadovoljio/la 65.98 3
22 Lara Budiša RED 9.51 9.03 6.5 1 15.95 12.41 11.50 39.86 Zadovoljio/la 65.90 3
23 Antonio Vinković RED 8.68 10.61 7.0 NA 15.93 15.60 7.96 39.48 Zadovoljio/la 65.78 3
24 Tin Muhar RED 7.80 11.88 5.5 1 16.22 11.60 11.71 39.53 Zadovoljio/la 65.71 3
25 Petar Svenšek RED 8.52 11.02 10.0 1 14.00 14.55 6.37 34.92 Zadovoljio/la 65.46 3
26 Sven Pavlić RED 9.51 8.74 7.5 NA 16.10 13.81 9.50 39.41 Zadovoljio/la 65.16 3
27 Marko Sokser RED 8.35 10.84 10.0 1 15.16 13.14 6.58 34.88 Zadovoljio/la 65.07 2
28 Dario Vučina RED 8.69 7.30 7.0 NA 16.58 13.99 11.39 41.96 Zadovoljio/la 64.95 3
29 Luka Šulentić RED 8.60 9.17 8.5 1 15.70 12.87 8.89 37.45 Zadovoljio/la 64.73 3
30 Filip Svetlečić RED 9.08 8.88 9.5 NA 14.77 13.26 7.81 35.84 Zadovoljio/la 63.30 3
31 Azra Gašparić RED 8.22 11.71 6.0 NA 11.69 12.19 12.96 36.84 Zadovoljio/la 62.77 2
32 Martina Vincijanović RED 8.16 8.38 7.5 1 18.67 12.35 6.57 37.59 Zadovoljio/la 62.63 3
33 Jan Posavec RED 8.64 9.03 9.5 1 17.08 6.75 10.18 34.01 Zadovoljio/la 62.18 3
34 David Brckan RED 7.35 11.12 9.0 1 12.78 10.91 9.57 33.25 Zadovoljio/la 61.73 3
35 Karlo Košulj RED 8.31 8.56 4.5 1 15.83 12.54 10.48 38.85 Zadovoljio/la 61.22 3
36 Petra Skoko RED 9.55 8.79 8.5 1 14.08 7.07 12.19 33.34 Zadovoljio/la 61.18 3
37 Jan Pobi RED 9.21 9.54 9.5 1 10.67 12.05 9.12 31.85 Zadovoljio/la 61.09 3
38 Tin Barbarić RED 7.90 11.06 3.5 NA 15.78 14.51 7.21 37.50 Zadovoljio/la 59.96 2
39 Antonio Martinaga RED 9.45 9.52 7.0 1 11.27 12.07 9.57 32.91 Zadovoljio/la 59.88 2
40 Mirta Vuković RED 7.06 10.35 8.0 NA 14.63 9.97 9.61 34.21 Zadovoljio/la 59.62 2
41 Lucia Andročec RED 9.11 10.68 5.0 1 14.08 7.62 12.09 33.79 Zadovoljio/la 59.58 2
42 Matej Ćutuk RED 9.61 11.13 6.0 NA 11.33 11.39 10.07 32.78 Zadovoljio/la 59.53 2
43 Jakov Malović RED 8.18 9.27 4.5 1 12.97 10.96 12.47 36.39 Zadovoljio/la 59.35 2
44 Niko Ivančić RED 4.91 10.63 6.0 NA 16.67 11.42 9.46 37.55 Zadovoljio/la 59.09 2
45 Luka Šuto RED 8.70 8.49 9.5 NA 14.77 8.79 8.07 31.62 Zadovoljio/la 58.32 2
46 Jakov Zoričić RED 8.03 6.86 6.5 NA 13.22 7.90 15.69 36.81 Zadovoljio/la 58.20 2
47 Vid Matanić RED 7.64 8.71 9.5 1 10.40 12.25 8.54 31.19 Zadovoljio/la 58.04 2
48 Adrian Stojić RED 5.32 10.72 4.0 1 13.86 12.77 10.17 36.79 Zadovoljio/la 57.84 2
49 Nikola Polonijo RED 7.80 9.53 5.0 NA 16.00 11.84 7.61 35.45 Zadovoljio/la 57.78 2
50 Filip Markić RED 6.49 8.45 8.0 1 16.51 8.29 9.00 33.80 Zadovoljio/la 57.74 2
51 Domagoj Lovošević RED 9.55 9.62 6.0 1 11.97 13.01 6.57 31.56 Zadovoljio/la 57.72 2
52 Matej Starčević RED 9.15 7.60 7.0 NA 16.93 10.94 5.58 33.45 Zadovoljio/la 57.20 2
53 Laura Benić RED 8.83 8.57 6.5 1 15.76 6.19 10.26 32.20 Zadovoljio/la 57.11 2
54 Marin Vabec RED 5.50 9.03 8.5 NA 11.87 12.35 9.84 34.06 Zadovoljio/la 57.09 2
55 Matej Šanko RED 9.67 7.65 10.0 1 10.02 7.70 10.99 28.70 Zadovoljio/la 57.03 2
56 Anja Svetličić RED 9.12 11.75 7.0 NA 10.72 7.35 10.93 29.00 Zadovoljio/la 56.87 2
57 Luka Hrupec RED 7.46 10.63 9.0 1 14.00 7.97 6.00 27.97 Zadovoljio/la 56.06 2
58 Mateo Čuvalo RED 6.64 8.16 5.0 1 13.80 12.84 7.69 34.33 Zadovoljio/la 55.13 2
59 Martin Trtanj RED 8.56 4.96 8.5 1 14.08 9.24 8.60 31.92 Zadovoljio/la 54.94 2
60 Veronika Dumić RED 8.94 9.01 6.5 1 12.42 7.90 9.05 29.37 Zadovoljio/la 54.82 2
61 Lana Savić RED 8.25 6.72 8.0 1 14.87 10.90 5.01 30.78 Zadovoljio/la 54.75 2
62 Nikola Bilić RED 6.81 11.94 6.5 1 12.83 8.05 7.58 28.47 Zadovoljio/la 54.71 2
63 Martin Premar RED 8.84 5.19 9.0 1 12.33 9.44 8.50 30.28 Zadovoljio/la 54.30 2
64 Karlo Vlah RED 8.05 8.76 6.0 NA 14.47 9.09 7.86 31.42 Zadovoljio/la 54.23 2
65 Marko Stipić RED 9.04 3.95 NA 1 15.50 14.94 9.57 40.01 Zadovoljio/la 54.00 2
66 Antonio Bobek RED 7.18 4.86 6.5 1 13.02 11.65 9.75 34.42 Zadovoljio/la 53.96 2
67 Marko Kovačić RED 7.88 8.76 6.5 1 12.95 8.40 8.36 29.71 Zadovoljio/la 53.85 2
68 Ivan Kraker RED 8.11 7.35 6.5 1 9.13 12.58 9.06 30.77 Zadovoljio/la 53.73 2
69 Ada Tikvan RED 8.45 7.66 7.0 1 14.42 8.82 6.38 29.62 Zadovoljio/la 53.73 2
70 Roberto Šandro RED 7.99 5.42 7.5 1 12.17 10.46 8.99 31.61 Zadovoljio/la 53.53 2
71 Nikola Lazar RED 9.02 9.33 6.5 1 13.20 9.41 5.04 27.65 Zadovoljio/la 53.50 2
72 Maksim Kos RED 8.56 7.88 8.0 1 11.94 9.89 6.18 28.00 Zadovoljio/la 53.45 2
73 Leonardo Petran RED 7.94 6.83 7.0 1 10.17 9.37 10.74 30.28 Zadovoljio/la 53.05 2
74 Dorian Brezovec RED 7.64 5.85 6.0 1 12.05 10.83 9.65 32.53 Zadovoljio/la 53.02 2
75 Lana Ljubičić RED 9.12 5.35 7.5 1 7.92 10.89 10.83 29.64 Zadovoljio/la 52.61 2
76 Šime Jović RED 7.51 9.10 NA 1 13.86 12.93 8.07 34.86 Zadovoljio/la 52.47 2
77 Mihael Novoselec RED 7.55 7.26 8.0 1 12.42 10.95 5.25 28.62 Zadovoljio/la 52.43 2
78 Andrej Pavešić RED 7.28 10.58 6.5 1 11.50 10.92 4.50 26.92 Zadovoljio/la 52.28 2
79 Sara Mršić RED 8.78 5.60 7.5 1 8.00 9.45 11.75 29.20 Zadovoljio/la 52.08 2
80 Josip Abramović RED 9.02 11.28 4.0 1 9.68 7.90 9.17 26.76 Zadovoljio/la 52.05 2
81 Danijel Ciberlin Ivanić RED 8.47 7.78 7.0 1 13.25 5.63 8.55 27.43 Zadovoljio/la 51.68 2
82 Antonio Đimbrek RED 6.95 12.10 6.0 1 9.03 10.51 5.95 25.49 Zadovoljio/la 51.54 2
83 David Šimičević RED 3.90 9.68 7.0 1 11.10 11.21 7.17 29.48 Zadovoljio/la 51.06 2
84 Mario Trošt RED 7.96 8.63 9.0 NA 8.52 8.20 8.43 25.16 Zadovoljio/la 50.74 2
85 Franka Marciuš RED 7.81 5.49 7.5 1 8.73 9.29 10.86 28.88 Zadovoljio/la 50.68 2
86 Špiro Pravdić RED 7.96 4.53 10.0 NA 12.37 9.65 6.17 28.19 Zadovoljio/la 50.68 2
87 Lovro Ivanković RED 7.84 3.44 8.5 NA 11.13 11.10 8.15 30.39 Zadovoljio/la 50.16 2
88 Sara Krišto RED 6.36 6.64 7.5 1 9.30 11.65 7.68 28.62 Zadovoljio/la 50.13 2
89 Bojan Horvat RED 7.81 9.25 4.0 1 10.83 7.22 9.95 28.00 Zadovoljio/la 50.06 2
90 Jakov Marčan RED 6.42 7.67 5.5 1 13.05 9.43 6.96 29.45 Zadovoljio/la 50.03 2
91 Zlatko Pračić RAZ 0.00 0.75 10.0 1 13.60 14.66 6.17 34.43 Zadovoljio/la 45.43 3
92 Mia Peharec RAZ 1.23 6.64 5.0 1 13.28 9.22 9.58 32.08 Zadovoljio/la 38.08 3
93 Emilia Jelačić RAZ 4.93 8.63 10.0 NA 14.55 5.91 7.06 27.52 Zadovoljio/la 37.52 3
94 Matteo Lež IZV 0.00 0.00 8.0 NA 13.95 17.96 4.59 36.50 Zadovoljio/la 44.50 3
95 Dino Pizek IZV 0.00 NA 7.5 NA 13.42 13.80 5.49 32.72 Zadovoljio/la 40.21 2

Pad na usmenom

Studenti koji su pali na usmenom ispitu
id Ime Prezime Status mail DZ KVIZ ESEJ ANKETA KOL1 KOL2 KOL3 KOL USMENI UKUPNO OCJENA
1 Dorian Karnik RED 7.79 12.03 6.0 NA 15.00 14.69 7.40 37.10 Nije zadovoljio/la 62.91 1
2 Filip Husnjak RED 7.08 11.28 9.5 1 12.35 12.33 5.75 30.43 Nije zadovoljio/la 59.29 1
3 Franjo Čotić RED 5.25 4.00 5.0 1 16.10 14.67 13.08 43.85 Nije zadovoljio/la 59.10 1
4 Vedran Bogdanović RED 8.47 11.97 6.5 1 11.85 11.53 7.58 30.96 Nije zadovoljio/la 58.90 1
5 Rafael Bistričić RED 7.65 11.70 6.5 1 12.67 9.09 9.42 31.17 Nije zadovoljio/la 58.03 1
6 Larija Jukić RED 7.43 9.52 9.5 1 14.09 8.60 7.78 30.47 Nije zadovoljio/la 57.92 1
7 Ennio David Komljenović RED 8.24 12.33 6.5 1 14.60 6.44 8.29 29.32 Nije zadovoljio/la 57.40 1
8 David Sedlan RED 7.64 3.59 9.0 NA 9.27 9.62 17.75 36.64 Nije zadovoljio/la 56.87 1
9 Antonio Dekanić RED 6.64 9.20 5.0 1 13.75 10.03 8.92 32.69 Nije zadovoljio/la 54.54 1
10 Leonardo Mihalina RED 5.45 9.91 9.0 1 14.04 8.90 5.25 28.19 Nije zadovoljio/la 53.55 1
11 Jan Buruš RED 6.58 8.80 6.5 1 9.70 9.22 11.61 30.52 Nije zadovoljio/la 53.41 1
12 Val Bekić RED 5.63 8.33 7.5 1 13.01 11.09 6.58 30.69 Nije zadovoljio/la 53.14 1
13 Ivan Cimerman RED 8.02 9.08 7.0 NA 14.04 10.67 4.25 28.96 Nije zadovoljio/la 53.06 1
14 Teo Galović RED 8.17 11.14 4.5 1 9.82 7.19 11.06 28.06 Nije zadovoljio/la 52.88 1
15 Lovro Špiljak RED 9.55 6.92 8.5 1 13.02 8.50 5.35 26.86 Nije zadovoljio/la 52.84 1
16 Boris Šarić RED 8.70 7.65 8.0 1 11.03 10.05 6.29 27.37 Nije zadovoljio/la 52.72 1
17 Nikola Brezovec RED 7.40 10.30 6.5 1 9.93 9.85 7.42 27.20 Nije zadovoljio/la 52.40 1
18 Viktoria Moguš RED 7.35 8.18 9.5 1 11.77 11.22 3.24 26.23 Nije zadovoljio/la 52.26 1
19 Jakov Štefanko RED 7.57 12.25 6.5 NA 12.40 9.70 3.67 25.77 Nije zadovoljio/la 52.09 1
20 Ema Andjelković RED 7.46 5.72 7.5 1 15.14 7.49 7.60 30.22 Nije zadovoljio/la 51.91 1
21 Roko Vlahov RED 8.19 7.39 5.0 1 12.72 6.62 10.85 30.19 Nije zadovoljio/la 51.77 1
22 Ivan Vlahović RED 8.46 2.99 4.0 1 12.35 11.43 11.35 35.13 Nije zadovoljio/la 51.58 1
23 Luka Mlinar RED 7.32 5.69 5.0 1 12.38 9.67 10.47 32.52 Nije zadovoljio/la 51.53 1
24 Dino Habijanac RED 8.13 6.65 6.5 NA 10.13 11.17 8.88 30.19 Nije zadovoljio/la 51.46 1
25 Fran Uljarević RED 8.35 8.63 7.0 1 11.92 7.27 7.21 26.41 Nije zadovoljio/la 51.38 1
26 Karlo Mišić RED 8.22 7.70 4.0 1 14.58 6.75 9.03 30.36 Nije zadovoljio/la 51.28 1
27 Mia Šižgorić RED 6.93 4.73 6.5 1 9.00 5.55 17.33 31.88 Nije zadovoljio/la 51.04 1
28 Matko Grbić RED 1.74 10.81 5.5 1 11.33 11.18 9.42 31.93 Nije zadovoljio/la 50.98 1
29 Ivan Leško RED 8.84 4.59 6.5 1 13.56 10.15 6.29 29.99 Nije zadovoljio/la 50.93 1
30 Janko Antonina RED 6.40 7.01 6.0 NA 14.68 8.94 7.81 31.42 Nije zadovoljio/la 50.84 1
31 Marko Žuraj RED 7.20 10.82 4.0 1 9.63 9.59 8.45 27.67 Nije zadovoljio/la 50.69 1
32 Juraj Vito Kuhtić RED 9.36 8.29 7.0 1 6.77 9.00 9.24 25.00 Nije zadovoljio/la 50.66 1
33 Filip Levis RED 5.87 7.58 4.5 NA 14.22 10.30 7.83 32.35 Nije zadovoljio/la 50.30 1
34 Lovro Sverić RED 8.35 9.14 5.0 1 9.83 7.65 9.25 26.73 Nije zadovoljio/la 50.22 1
35 Andrea Klinac RED 7.38 9.05 6.0 NA 11.06 10.92 5.71 27.69 Nije zadovoljio/la 50.12 1

Gustoće

Column

Funkcije gustoća po aktivnostima (redovni studenti)

Column

Broj studenata po statusu

Status n
IZV 14
RAZ 16
RED 334

Brojčane vrijednosti (redovni studenti)

aktivnost BROJ MEAN MEDIAN MIN MAX
KOL1 308 10.51 10.54 0 19.33
KOL2 297 8.43 8.09 0 19.15
KOL3 247 7.34 7.58 0 17.75
KVIZ 329 6.30 6.09 0 18.27
DZ 334 5.83 6.59 0 10.00
ESEJ 242 6.17 6.00 1 10.00

Violine

Ukupni bodovi

Column

Frekvencije ukupnih bodova (redovni studenti)

Ocjene (redovni)

Column

Distribucija bodova

Korelacije

Column

Korelacije

Column

p-vrijednosti korelacija

Usmeni - grafički prikaz

Usmeni (grafički prikaz)

Pad i prolaz na usmenom

Column 1

Frekvencije bodova po aktivnostima studenata koji su pali na usmenom

Column 2

Frekvencije bodova po aktivnostima studenata koji su prošli na usmenom

Esej po kategorijama

Column 1

četvrtine

kvartili 1

kvartili 2

Column 2

Kategorije

Broj redovnih studenata koji su pisali esej (četvrtine)
Kategorija broj studenata pisalo esej
0 ≤ UKUPNO < 25 85 16
25 ≤ UKUPNO < 50 123 102
50 ≤ UKUPNO < 75 120 118
75 ≤ UKUPNO ≤ 100 6 6
Broj redovnih studenata koji su pisali esej (kvartili 1)
Kategorija broj studenata pisalo esej
0 ≤ UKUPNO < 24.465 84 15
24.465 ≤ UKUPNO < 43.985 83 62
43.985 ≤ UKUPNO < 53.12 83 82
53.12 ≤ UKUPNO ≤ 82.82 84 83
Broj redovnih studenata koji su pisali esej (kvartili 2)
Kategorija broj studenata
10.48 ≤ UKUPNO < 38.355 61
38.355 ≤ UKUPNO < 50.12 60
50.12 ≤ UKUPNO < 57.075 60
57.075 ≤ UKUPNO ≤ 82.82 61

Ukupno

Esej (ukupno u sumi za redovne studente)
pisalo esej nije pisalo esej
242 92
Esej (ukupno u sumi za redovne studente u postocima)
pisalo esej nije pisalo esej
72% 28%

Kvartili ukupnih bodova redovnih studenata

    0%    25%    50%    75%   100% 
 0.000 24.465 43.985 53.120 82.820

Kvartili ukupnih bodova redovnih studenata koji su pisali esej

    0%    25%    50%    75%   100% 
10.480 38.355 50.120 57.075 82.820

Pojedinačni kvizovi

Frekvencije bodova po kvizovima

Pojedinačni kvizovi po kategorijama

Frekvencije bodova po kvizovima i kategorijama

Pristupanje kvizovima

Column 1

Općenito

Varijable

Studenti

Varijable (kategorije u %)

Column 2

Općenito (klasterirano)

Varijable (kategorije)

Studenti (kategorije)

Pristupanje kvizovima - presječni pogled

Pristupanje kvizovima - presječni pogled

Pletenica

Pletenica

Učenje

Column 1

struktura 1

struktura 2

struktura 3

struktura 4

Column 2

Info

Opisi varijabli

  • KOL1 - faktorska varijabla s 4 klase napravljene prema četvrtinama ukupnih bodova na prvom kolokviju
  • KOL2 - faktorska varijabla s 4 klase napravljene prema četvrtinama ukupnih bodova na drugom kolokviju
  • KOL3 - faktorska varijabla s 4 klase napravljene prema četvrtinama ukupnih bodova na trećem kolokviju
  • KVIZ1 - faktorska varijabla s 4 klase napravljene prema četvrtinama ukupnih bodova na kvizovima prije prvog kolokvija
  • KVIZ2 - faktorska varijabla s 4 klase napravljene prema četvrtinama ukupnih bodova na kvizovima između prvog i drugog kolokvija
  • KVIZ3 - faktorska varijabla s 4 klase napravljene prema četvrtinama ukupnih bodova na kvizovima nakon drugog kolokvija
  • ESEJ - faktorska varijabla s 4 klase napravljene prema četvrtinama ukupnih bodova na eseju

Opisi struktura

  • struktura 1 - učenje pomoću hc algoritma na praznom digrafu
  • struktura 2 - učenje pomoću hc algoritma na praznom digrafu uz zabranjene lukove (KOL1, KVIZ1), (KOL2, KVIZ1), (KOL3, KVIZ1), (ESEJ, KVIZ1), (KOL2, KVIZ2), (KOL3, KVIZ2), (KVIZ3, KVIZ2), (ESEJ, KVIZ2)
  • struktura 3 - učenje pomoću gs algoritma na praznom digrafu, neki bridovi nemaju orijentaciju pa moramo sami odlučiti koju ćemo uzeti
  • struktura 4 - odabrane orijentacije za bridove iz strukture 3

BIC score

struktura 1

[1] -2511.71

struktura 2

[1] -2529.825

struktura 4

[1] -3120.295

Distribucije - struktura 1

Column 1

KOL1

KOL2 | KOL1

KOL3 | KOL2

KVIZ1 | KOL2

KVIZ2 | KOL2

KVIZ3 | ESEJ

ESEJ | KOL2

Column 2

struktura 1

marginalne distribucije

Distribucije - struktura 2

Column 1

KOL1 | KVIZ1

KOL2 | KOL1

KOL3 | KOL2

KVIZ1

KVIZ2 | KOL1

KVIZ3 | ESEJ

ESEJ | KOL2

Column 2

struktura 2

marginalne distribucije

Distribucije - struktura 4

Column 1

KOL1

KOL2 | KOL1,KVIZ1

KOL3 | KOL2,KVIZ2,KVIZ3

KVIZ1

KVIZ2

KVIZ3 | ESEJ

ESEJ

Column 2

struktura 4

marginalne distribucije

Postavljanje upita - struktura 2

Column 1

P(ESEJ | KOL1, KOL2)

, , KOL2 = 1

    KOL1
ESEJ           1           2           3           4
   1 0.689306358 0.689306358 0.689306358 0.689306358
   2 0.203757225 0.203757225 0.203757225 0.203757225
   3 0.099710983 0.099710983 0.099710983 0.099710983
   4 0.007225434 0.007225434 0.007225434 0.007225434

, , KOL2 = 2

    KOL1
ESEJ         1         2         3         4
   1 0.2108150 0.2108150 0.2108150 0.2108150
   2 0.2547022 0.2547022 0.2547022 0.2547022
   3 0.3738245 0.3738245 0.3738245 0.3738245
   4 0.1606583 0.1606583 0.1606583 0.1606583

, , KOL2 = 3

    KOL1
ESEJ          1          2          3          4
   1 0.09242424 0.09242424 0.09242424 0.09242424
   2 0.21363636 0.21363636 0.21363636 0.21363636
   3 0.40757576 0.40757576 0.40757576 0.40757576
   4 0.28636364 0.28636364 0.28636364 0.28636364

, , KOL2 = 4

    KOL1
ESEJ          1          2          3          4
   1 0.04032258 0.04032258 0.04032258 0.04032258
   2 0.04032258 0.04032258 0.04032258 0.04032258
   3 0.62096774 0.62096774 0.62096774 0.62096774
   4 0.29838710 0.29838710 0.29838710 0.29838710

Column 2

P(ESEJ, KOL1, KOL2)

, , ESEJ = 1

    KOL2
KOL1           1           2           3            4
   1 0.073389085 0.006511366 0.000167922 0.0000732605
   2 0.081404276 0.043281432 0.003123348 0.0000732605
   3 0.015278956 0.045119936 0.013333003 0.0005421277
   4 0.003256171 0.002834359 0.005541424 0.0011282117

, , ESEJ = 2

    KOL2
KOL1           1           2            3            4
   1 0.021693629 0.007866892 0.0003881475 0.0000732605
   2 0.024062899 0.052291693 0.0072195428 0.0000732605
   3 0.004516421 0.054512933 0.0308189086 0.0005421277
   4 0.000962516 0.003424412 0.0128088663 0.0011282117

, , ESEJ = 3

    KOL2
KOL1            1           2            3           4
   1 0.0106160312 0.011546177 0.0007405083 0.001128212
   2 0.0117754613 0.076748116 0.0137734540 0.001128212
   3 0.0022101635 0.080008213 0.0587963575 0.008348767
   4 0.0004710185 0.005025983 0.0244367733 0.017374461

, , ESEJ = 4

    KOL2
KOL1            1           2            3            4
   1 7.692776e-04 0.004962193 0.0005202828 0.0005421277
   2 8.532943e-04 0.032983991 0.0096772595 0.0005421277
   3 1.601568e-04 0.034385081 0.0413104519 0.0040117451
   4 3.413177e-05 0.002160014 0.0171693314 0.0083487669

Predikcije eseja - struktura 1

Column 1

Metrike na testnom i trening skupu

# A tibble: 9 × 4
  .metric     .estimator trening   test
  <chr>       <chr>        <dbl>  <dbl>
1 sens        macro       0.3884 0.4260
2 precision   macro       0.3642 0.3393
3 spec        macro       0.8088 0.8242
4 accuracy    multiclass  0.4659 0.5059
5 f_meas      macro       0.3377 0.3667
6 mcc         multiclass  0.2529 0.3176
7 kap         multiclass  0.2351 0.2950
8 roc_auc     hand_till   0.7107 0.7050
9 mn_log_loss multiclass  1.075  1.170

Confusion matrix

Column 2

ROC curve

Gain curve

Predikcije eseja - struktura 2

Column 1

Metrike na testnom i trening skupu

# A tibble: 9 × 4
  .metric     .estimator trening   test
  <chr>       <chr>        <dbl>  <dbl>
1 sens        macro       0.3916 0.4067
2 precision   macro       0.3568 0.3768
3 spec        macro       0.8115 0.8166
4 accuracy    multiclass  0.4739 0.4941
5 f_meas      macro       0.3351 0.3385
6 mcc         multiclass  0.2653 0.2980
7 kap         multiclass  0.2453 0.2683
8 roc_auc     hand_till   0.7121 0.7122
9 mn_log_loss multiclass  1.073  1.166

Confusion matrix

Column 2

ROC curve

Gain curve

Predikcije eseja - struktura 4

Column 1

Metrike na testnom i trening skupu

# A tibble: 9 × 4
  .metric     .estimator trening   test
  <chr>       <chr>        <dbl>  <dbl>
1 sens        macro       0.3995 0.3786
2 precision   macro       0.4931 0.4707
3 spec        macro       0.8160 0.8075
4 accuracy    multiclass  0.4940 0.4706
5 f_meas      macro       0.6092 0.5802
6 mcc         multiclass  0.3025 0.2623
7 kap         multiclass  0.2656 0.2305
8 roc_auc     hand_till   0.6609 0.7050
9 mn_log_loss multiclass  1.172  1.170

Confusion matrix

Column 2

ROC curve

Gain curve

Opis modela

Opis modela

Prediktori

  • KVIZ - suma bodova na kvizovima
  • DZ - suma bodova na domaćim zadaćama
  • KOL1 - broj bodova na prvom kolokviju
  • KOL2 - broj bodova na drugom kolokviju
  • KOL3 - broj bodova na trećem kolokviju

Response - Klasifikacija studenata na temelju broja bodova eseju.

  • 1 - ako je na eseju ukupni broj bodova unutar intervala [0, 2.5]
  • 2 - ako je na eseju ukupni broj bodova unutar intervala (2.5, 5]
  • 3 - ako je na eseju ukupni broj bodova unutar intervala (5, 7.5]
  • 4 - ako je na eseju ukupni broj bodova unutar intervala (7.5, 10]

Hiperparametri - za odabir optimalne kombinacije hiperparametara napravljen je cross-validation s 10 kutija pri čemu je na slučajni način isprobano 1000 kombinacija hiperparametara mtry, min_n i trees. Najbolji model je odabran s obzirom na roc_auc metriku.

  • mtry - uzimane su vrijednosti iz skupa {2, 3, 4}
  • trees - uzimani su prirodni brojevi između 400 i 1500
  • min_n - uzimani su prirodni brojevi između 2 i 40.

Hiperparametri

Column 1

Testirani hiperparametri

Column 2

200 najboljih modela za roc_auc metriku

# A tibble: 200 × 9
     mtry trees min_n .metric .estimator   mean     n std_err .config           
    <int> <int> <int> <chr>   <chr>       <dbl> <int>   <dbl> <chr>             
  1     3   888     2 roc_auc hand_till  0.7644    10 0.01617 Preprocessor1_Mod…
  2     4  1149     2 roc_auc hand_till  0.7630    10 0.01684 Preprocessor1_Mod…
  3     2  1414     4 roc_auc hand_till  0.7630    10 0.01513 Preprocessor1_Mod…
  4     4   748     4 roc_auc hand_till  0.7629    10 0.01666 Preprocessor1_Mod…
  5     4   614     3 roc_auc hand_till  0.7626    10 0.01643 Preprocessor1_Mod…
  6     2  1330     4 roc_auc hand_till  0.7624    10 0.01502 Preprocessor1_Mod…
  7     3  1392     4 roc_auc hand_till  0.7623    10 0.01743 Preprocessor1_Mod…
  8     3   861     2 roc_auc hand_till  0.7623    10 0.01624 Preprocessor1_Mod…
  9     3  1052     3 roc_auc hand_till  0.7622    10 0.01592 Preprocessor1_Mod…
 10     2  1372     2 roc_auc hand_till  0.7622    10 0.01613 Preprocessor1_Mod…
 11     4   656     6 roc_auc hand_till  0.7619    10 0.01471 Preprocessor1_Mod…
 12     2  1196     3 roc_auc hand_till  0.7619    10 0.01554 Preprocessor1_Mod…
 13     4  1394     3 roc_auc hand_till  0.7619    10 0.01674 Preprocessor1_Mod…
 14     2   792     4 roc_auc hand_till  0.7617    10 0.01430 Preprocessor1_Mod…
 15     4   497     6 roc_auc hand_till  0.7614    10 0.01715 Preprocessor1_Mod…
 16     3   839     3 roc_auc hand_till  0.7614    10 0.01582 Preprocessor1_Mod…
 17     2  1215     5 roc_auc hand_till  0.7611    10 0.01443 Preprocessor1_Mod…
 18     4   666     2 roc_auc hand_till  0.7610    10 0.01810 Preprocessor1_Mod…
 19     2   569     7 roc_auc hand_till  0.7610    10 0.01530 Preprocessor1_Mod…
 20     3  1467     6 roc_auc hand_till  0.7609    10 0.01577 Preprocessor1_Mod…
 21     4   925     3 roc_auc hand_till  0.7607    10 0.01717 Preprocessor1_Mod…
 22     2  1436     4 roc_auc hand_till  0.7607    10 0.01509 Preprocessor1_Mod…
 23     2   721     3 roc_auc hand_till  0.7606    10 0.01577 Preprocessor1_Mod…
 24     2   733     8 roc_auc hand_till  0.7606    10 0.01494 Preprocessor1_Mod…
 25     4  1439     3 roc_auc hand_till  0.7606    10 0.01741 Preprocessor1_Mod…
 26     2   608     4 roc_auc hand_till  0.7606    10 0.01649 Preprocessor1_Mod…
 27     3   437     5 roc_auc hand_till  0.7606    10 0.01409 Preprocessor1_Mod…
 28     3   548     6 roc_auc hand_till  0.7605    10 0.01493 Preprocessor1_Mod…
 29     3   595     7 roc_auc hand_till  0.7604    10 0.01553 Preprocessor1_Mod…
 30     3  1289     3 roc_auc hand_till  0.7603    10 0.01675 Preprocessor1_Mod…
 31     4  1311     3 roc_auc hand_till  0.7603    10 0.01593 Preprocessor1_Mod…
 32     3  1229     4 roc_auc hand_till  0.7603    10 0.01601 Preprocessor1_Mod…
 33     4  1469     3 roc_auc hand_till  0.7602    10 0.01685 Preprocessor1_Mod…
 34     2  1215     4 roc_auc hand_till  0.7602    10 0.01521 Preprocessor1_Mod…
 35     4  1281     5 roc_auc hand_till  0.7602    10 0.01665 Preprocessor1_Mod…
 36     3  1370     4 roc_auc hand_till  0.7601    10 0.01644 Preprocessor1_Mod…
 37     2   551     5 roc_auc hand_till  0.7601    10 0.01563 Preprocessor1_Mod…
 38     2   981     6 roc_auc hand_till  0.7601    10 0.01491 Preprocessor1_Mod…
 39     4   936     6 roc_auc hand_till  0.7600    10 0.01787 Preprocessor1_Mod…
 40     3  1286     5 roc_auc hand_till  0.7600    10 0.01672 Preprocessor1_Mod…
 41     4   885     5 roc_auc hand_till  0.7599    10 0.01646 Preprocessor1_Mod…
 42     3   694     3 roc_auc hand_till  0.7599    10 0.01645 Preprocessor1_Mod…
 43     2   576     3 roc_auc hand_till  0.7598    10 0.01512 Preprocessor1_Mod…
 44     3   800     5 roc_auc hand_till  0.7598    10 0.01545 Preprocessor1_Mod…
 45     4  1314     6 roc_auc hand_till  0.7597    10 0.01576 Preprocessor1_Mod…
 46     4  1286     2 roc_auc hand_till  0.7597    10 0.01642 Preprocessor1_Mod…
 47     2   508     3 roc_auc hand_till  0.7595    10 0.01611 Preprocessor1_Mod…
 48     4   575     5 roc_auc hand_till  0.7595    10 0.01562 Preprocessor1_Mod…
 49     4   758     9 roc_auc hand_till  0.7594    10 0.01578 Preprocessor1_Mod…
 50     3  1238     7 roc_auc hand_till  0.7594    10 0.01563 Preprocessor1_Mod…
 51     2  1321     4 roc_auc hand_till  0.7594    10 0.01620 Preprocessor1_Mod…
 52     2   412     5 roc_auc hand_till  0.7594    10 0.01439 Preprocessor1_Mod…
 53     4   994     5 roc_auc hand_till  0.7594    10 0.01659 Preprocessor1_Mod…
 54     4  1293     7 roc_auc hand_till  0.7594    10 0.01535 Preprocessor1_Mod…
 55     4  1486     6 roc_auc hand_till  0.7593    10 0.01640 Preprocessor1_Mod…
 56     4   879     4 roc_auc hand_till  0.7593    10 0.01675 Preprocessor1_Mod…
 57     3  1292     8 roc_auc hand_till  0.7592    10 0.01652 Preprocessor1_Mod…
 58     3   721     3 roc_auc hand_till  0.7592    10 0.01589 Preprocessor1_Mod…
 59     3   907     2 roc_auc hand_till  0.7592    10 0.01638 Preprocessor1_Mod…
 60     2   470     6 roc_auc hand_till  0.7592    10 0.01462 Preprocessor1_Mod…
 61     4  1028     6 roc_auc hand_till  0.7591    10 0.01655 Preprocessor1_Mod…
 62     4   417     3 roc_auc hand_till  0.7591    10 0.01685 Preprocessor1_Mod…
 63     3  1373     4 roc_auc hand_till  0.7591    10 0.01455 Preprocessor1_Mod…
 64     3  1353     2 roc_auc hand_till  0.7591    10 0.01585 Preprocessor1_Mod…
 65     2  1454     2 roc_auc hand_till  0.7589    10 0.01448 Preprocessor1_Mod…
 66     2   696     2 roc_auc hand_till  0.7589    10 0.01565 Preprocessor1_Mod…
 67     4   687     2 roc_auc hand_till  0.7588    10 0.01622 Preprocessor1_Mod…
 68     3  1019     8 roc_auc hand_till  0.7588    10 0.01558 Preprocessor1_Mod…
 69     3   790     4 roc_auc hand_till  0.7588    10 0.01649 Preprocessor1_Mod…
 70     3  1226     6 roc_auc hand_till  0.7588    10 0.01570 Preprocessor1_Mod…
 71     3   949     6 roc_auc hand_till  0.7588    10 0.01545 Preprocessor1_Mod…
 72     3   914    10 roc_auc hand_till  0.7588    10 0.01606 Preprocessor1_Mod…
 73     4  1243     6 roc_auc hand_till  0.7587    10 0.01643 Preprocessor1_Mod…
 74     4   671     2 roc_auc hand_till  0.7586    10 0.01604 Preprocessor1_Mod…
 75     2  1189     4 roc_auc hand_till  0.7585    10 0.01548 Preprocessor1_Mod…
 76     4   507     4 roc_auc hand_till  0.7584    10 0.01512 Preprocessor1_Mod…
 77     2  1396     6 roc_auc hand_till  0.7583    10 0.01584 Preprocessor1_Mod…
 78     4  1048     3 roc_auc hand_till  0.7583    10 0.01594 Preprocessor1_Mod…
 79     2   887     2 roc_auc hand_till  0.7583    10 0.01642 Preprocessor1_Mod…
 80     4   475     8 roc_auc hand_till  0.7582    10 0.01491 Preprocessor1_Mod…
 81     4   995     3 roc_auc hand_till  0.7582    10 0.01589 Preprocessor1_Mod…
 82     2  1071     4 roc_auc hand_till  0.7582    10 0.01491 Preprocessor1_Mod…
 83     4   834     5 roc_auc hand_till  0.7582    10 0.01675 Preprocessor1_Mod…
 84     2  1254     6 roc_auc hand_till  0.7582    10 0.01582 Preprocessor1_Mod…
 85     4  1380     8 roc_auc hand_till  0.7581    10 0.01468 Preprocessor1_Mod…
 86     3   851     3 roc_auc hand_till  0.7581    10 0.01534 Preprocessor1_Mod…
 87     3  1106     2 roc_auc hand_till  0.7581    10 0.01700 Preprocessor1_Mod…
 88     3   403     7 roc_auc hand_till  0.7580    10 0.01670 Preprocessor1_Mod…
 89     3   499     4 roc_auc hand_till  0.7580    10 0.01583 Preprocessor1_Mod…
 90     3  1177     6 roc_auc hand_till  0.7579    10 0.01589 Preprocessor1_Mod…
 91     4   486     7 roc_auc hand_till  0.7578    10 0.01703 Preprocessor1_Mod…
 92     4  1329     5 roc_auc hand_till  0.7578    10 0.01622 Preprocessor1_Mod…
 93     4  1292     2 roc_auc hand_till  0.7578    10 0.01604 Preprocessor1_Mod…
 94     4  1237     8 roc_auc hand_till  0.7577    10 0.01577 Preprocessor1_Mod…
 95     2  1343     9 roc_auc hand_till  0.7577    10 0.01415 Preprocessor1_Mod…
 96     3   938     2 roc_auc hand_till  0.7576    10 0.01640 Preprocessor1_Mod…
 97     2   911     4 roc_auc hand_till  0.7575    10 0.01644 Preprocessor1_Mod…
 98     4  1310     6 roc_auc hand_till  0.7575    10 0.01567 Preprocessor1_Mod…
 99     3  1462     6 roc_auc hand_till  0.7574    10 0.01565 Preprocessor1_Mod…
100     4  1209     6 roc_auc hand_till  0.7574    10 0.01512 Preprocessor1_Mod…
101     2  1091     3 roc_auc hand_till  0.7573    10 0.01426 Preprocessor1_Mod…
102     2   985     7 roc_auc hand_till  0.7573    10 0.01462 Preprocessor1_Mod…
103     4   586     8 roc_auc hand_till  0.7573    10 0.01616 Preprocessor1_Mod…
104     3   443     5 roc_auc hand_till  0.7572    10 0.01483 Preprocessor1_Mod…
105     4   843     8 roc_auc hand_till  0.7572    10 0.01631 Preprocessor1_Mod…
106     3   603     3 roc_auc hand_till  0.7572    10 0.01378 Preprocessor1_Mod…
107     3   631     2 roc_auc hand_till  0.7571    10 0.01595 Preprocessor1_Mod…
108     2  1223     3 roc_auc hand_till  0.7570    10 0.01603 Preprocessor1_Mod…
109     2   517     6 roc_auc hand_till  0.7569    10 0.01637 Preprocessor1_Mod…
110     4   564     6 roc_auc hand_till  0.7569    10 0.01613 Preprocessor1_Mod…
111     3   826     9 roc_auc hand_till  0.7569    10 0.01423 Preprocessor1_Mod…
112     4  1438     7 roc_auc hand_till  0.7568    10 0.01606 Preprocessor1_Mod…
113     2   506     5 roc_auc hand_till  0.7568    10 0.01333 Preprocessor1_Mod…
114     3   430     2 roc_auc hand_till  0.7567    10 0.01752 Preprocessor1_Mod…
115     3   656     7 roc_auc hand_till  0.7566    10 0.01690 Preprocessor1_Mod…
116     4   445    10 roc_auc hand_till  0.7566    10 0.01555 Preprocessor1_Mod…
117     2   469     8 roc_auc hand_till  0.7565    10 0.01525 Preprocessor1_Mod…
118     3  1481     9 roc_auc hand_till  0.7565    10 0.01499 Preprocessor1_Mod…
119     4   635     6 roc_auc hand_till  0.7565    10 0.01507 Preprocessor1_Mod…
120     4  1330     2 roc_auc hand_till  0.7565    10 0.01688 Preprocessor1_Mod…
121     3   816     7 roc_auc hand_till  0.7565    10 0.01539 Preprocessor1_Mod…
122     4   553     8 roc_auc hand_till  0.7564    10 0.01712 Preprocessor1_Mod…
123     4  1187     7 roc_auc hand_till  0.7564    10 0.01572 Preprocessor1_Mod…
124     4  1364     2 roc_auc hand_till  0.7564    10 0.01653 Preprocessor1_Mod…
125     3   629     5 roc_auc hand_till  0.7564    10 0.01501 Preprocessor1_Mod…
126     3   899     6 roc_auc hand_till  0.7563    10 0.01530 Preprocessor1_Mod…
127     2   755     9 roc_auc hand_till  0.7563    10 0.01479 Preprocessor1_Mod…
128     3  1346    10 roc_auc hand_till  0.7563    10 0.01546 Preprocessor1_Mod…
129     3  1308     8 roc_auc hand_till  0.7562    10 0.01523 Preprocessor1_Mod…
130     3  1390    11 roc_auc hand_till  0.7562    10 0.01582 Preprocessor1_Mod…
131     2  1451     8 roc_auc hand_till  0.7561    10 0.01490 Preprocessor1_Mod…
132     4   507     6 roc_auc hand_till  0.7561    10 0.01686 Preprocessor1_Mod…
133     3  1295     5 roc_auc hand_till  0.7561    10 0.01552 Preprocessor1_Mod…
134     2  1084     5 roc_auc hand_till  0.7560    10 0.01539 Preprocessor1_Mod…
135     2   992     4 roc_auc hand_till  0.7560    10 0.01513 Preprocessor1_Mod…
136     2   628     5 roc_auc hand_till  0.7560    10 0.01521 Preprocessor1_Mod…
137     3  1346     9 roc_auc hand_till  0.7559    10 0.01477 Preprocessor1_Mod…
138     3  1016     8 roc_auc hand_till  0.7559    10 0.01501 Preprocessor1_Mod…
139     2  1082     7 roc_auc hand_till  0.7558    10 0.01626 Preprocessor1_Mod…
140     4   552     9 roc_auc hand_till  0.7558    10 0.01612 Preprocessor1_Mod…
141     2   732     7 roc_auc hand_till  0.7558    10 0.01266 Preprocessor1_Mod…
142     2   833     8 roc_auc hand_till  0.7558    10 0.01575 Preprocessor1_Mod…
143     4  1016     9 roc_auc hand_till  0.7557    10 0.01489 Preprocessor1_Mod…
144     4   504    10 roc_auc hand_till  0.7555    10 0.01605 Preprocessor1_Mod…
145     3   903     7 roc_auc hand_till  0.7554    10 0.01642 Preprocessor1_Mod…
146     2   598     6 roc_auc hand_till  0.7554    10 0.01397 Preprocessor1_Mod…
147     2  1009     6 roc_auc hand_till  0.7553    10 0.01460 Preprocessor1_Mod…
148     2  1015    10 roc_auc hand_till  0.7553    10 0.01466 Preprocessor1_Mod…
149     4   804     5 roc_auc hand_till  0.7553    10 0.01538 Preprocessor1_Mod…
150     4   460     3 roc_auc hand_till  0.7553    10 0.01526 Preprocessor1_Mod…
151     3  1409     8 roc_auc hand_till  0.7553    10 0.01467 Preprocessor1_Mod…
152     3   825     8 roc_auc hand_till  0.7551    10 0.01550 Preprocessor1_Mod…
153     4  1083    10 roc_auc hand_till  0.7551    10 0.01487 Preprocessor1_Mod…
154     3   424     5 roc_auc hand_till  0.7551    10 0.01689 Preprocessor1_Mod…
155     4   465     8 roc_auc hand_till  0.7551    10 0.01581 Preprocessor1_Mod…
156     3  1368     9 roc_auc hand_till  0.7550    10 0.01534 Preprocessor1_Mod…
157     3   645     6 roc_auc hand_till  0.7547    10 0.01638 Preprocessor1_Mod…
158     4  1105     9 roc_auc hand_till  0.7547    10 0.01653 Preprocessor1_Mod…
159     4   435     8 roc_auc hand_till  0.7546    10 0.01558 Preprocessor1_Mod…
160     4   564     8 roc_auc hand_till  0.7546    10 0.01636 Preprocessor1_Mod…
161     4  1433    12 roc_auc hand_till  0.7545    10 0.01527 Preprocessor1_Mod…
162     3   686     5 roc_auc hand_till  0.7545    10 0.01627 Preprocessor1_Mod…
163     4   405     7 roc_auc hand_till  0.7545    10 0.01454 Preprocessor1_Mod…
164     3  1101     7 roc_auc hand_till  0.7543    10 0.01547 Preprocessor1_Mod…
165     4  1390     9 roc_auc hand_till  0.7543    10 0.01567 Preprocessor1_Mod…
166     4  1478    11 roc_auc hand_till  0.7542    10 0.01440 Preprocessor1_Mod…
167     4   537     5 roc_auc hand_till  0.7541    10 0.01537 Preprocessor1_Mod…
168     2  1225    10 roc_auc hand_till  0.7541    10 0.01500 Preprocessor1_Mod…
169     2   746     7 roc_auc hand_till  0.7541    10 0.01410 Preprocessor1_Mod…
170     2   852     8 roc_auc hand_till  0.7540    10 0.01509 Preprocessor1_Mod…
171     2   774     6 roc_auc hand_till  0.7540    10 0.01604 Preprocessor1_Mod…
172     2   504     4 roc_auc hand_till  0.7540    10 0.01610 Preprocessor1_Mod…
173     4  1167     9 roc_auc hand_till  0.7540    10 0.01392 Preprocessor1_Mod…
174     4   955     9 roc_auc hand_till  0.7540    10 0.01463 Preprocessor1_Mod…
175     2  1420     8 roc_auc hand_till  0.7540    10 0.01481 Preprocessor1_Mod…
176     3   972    10 roc_auc hand_till  0.7539    10 0.01483 Preprocessor1_Mod…
177     3   740     7 roc_auc hand_till  0.7538    10 0.01568 Preprocessor1_Mod…
178     4   807     8 roc_auc hand_till  0.7538    10 0.01545 Preprocessor1_Mod…
179     2  1226     5 roc_auc hand_till  0.7537    10 0.01550 Preprocessor1_Mod…
180     2   406     9 roc_auc hand_till  0.7537    10 0.01430 Preprocessor1_Mod…
181     4   497    12 roc_auc hand_till  0.7537    10 0.01505 Preprocessor1_Mod…
182     4  1354     7 roc_auc hand_till  0.7537    10 0.01568 Preprocessor1_Mod…
183     4  1280     8 roc_auc hand_till  0.7536    10 0.01620 Preprocessor1_Mod…
184     4   513     8 roc_auc hand_till  0.7536    10 0.01401 Preprocessor1_Mod…
185     4  1077     9 roc_auc hand_till  0.7536    10 0.01442 Preprocessor1_Mod…
186     3  1128    11 roc_auc hand_till  0.7535    10 0.01467 Preprocessor1_Mod…
187     4   584    10 roc_auc hand_till  0.7535    10 0.01586 Preprocessor1_Mod…
188     4   742     9 roc_auc hand_till  0.7535    10 0.01594 Preprocessor1_Mod…
189     2   897     8 roc_auc hand_till  0.7534    10 0.01437 Preprocessor1_Mod…
190     2   869     9 roc_auc hand_till  0.7533    10 0.01399 Preprocessor1_Mod…
191     4   687     8 roc_auc hand_till  0.7533    10 0.01577 Preprocessor1_Mod…
192     4  1015     9 roc_auc hand_till  0.7533    10 0.01539 Preprocessor1_Mod…
193     4  1020    10 roc_auc hand_till  0.7532    10 0.01716 Preprocessor1_Mod…
194     2   933     6 roc_auc hand_till  0.7532    10 0.01649 Preprocessor1_Mod…
195     4   738     9 roc_auc hand_till  0.7532    10 0.01562 Preprocessor1_Mod…
196     3   546    11 roc_auc hand_till  0.7530    10 0.01544 Preprocessor1_Mod…
197     4  1213    10 roc_auc hand_till  0.7530    10 0.01618 Preprocessor1_Mod…
198     4   479     6 roc_auc hand_till  0.7530    10 0.01623 Preprocessor1_Mod…
199     2  1473     8 roc_auc hand_till  0.7529    10 0.01438 Preprocessor1_Mod…
200     2  1015     7 roc_auc hand_till  0.7529    10 0.01554 Preprocessor1_Mod…

200 najboljih modela za mcc metriku

# A tibble: 200 × 9
     mtry trees min_n .metric .estimator   mean     n std_err .config           
    <int> <int> <int> <chr>   <chr>       <dbl> <int>   <dbl> <chr>             
  1     2   723     9 mcc     multiclass 0.3409    10 0.02990 Preprocessor1_Mod…
  2     3  1128    11 mcc     multiclass 0.3384    10 0.03447 Preprocessor1_Mod…
  3     3   437     5 mcc     multiclass 0.3375    10 0.03124 Preprocessor1_Mod…
  4     2   833     8 mcc     multiclass 0.3360    10 0.02916 Preprocessor1_Mod…
  5     2  1082     7 mcc     multiclass 0.3346    10 0.03769 Preprocessor1_Mod…
  6     2  1290     9 mcc     multiclass 0.3345    10 0.03913 Preprocessor1_Mod…
  7     4  1390     9 mcc     multiclass 0.3334    10 0.03400 Preprocessor1_Mod…
  8     4  1083    10 mcc     multiclass 0.3325    10 0.03426 Preprocessor1_Mod…
  9     4   885    13 mcc     multiclass 0.3324    10 0.03584 Preprocessor1_Mod…
 10     3   633    13 mcc     multiclass 0.3323    10 0.03551 Preprocessor1_Mod…
 11     3   740     7 mcc     multiclass 0.3313    10 0.03461 Preprocessor1_Mod…
 12     3   826     9 mcc     multiclass 0.3305    10 0.04475 Preprocessor1_Mod…
 13     2   611    13 mcc     multiclass 0.3298    10 0.04057 Preprocessor1_Mod…
 14     2   485     9 mcc     multiclass 0.3294    10 0.02609 Preprocessor1_Mod…
 15     3  1035    13 mcc     multiclass 0.3292    10 0.02773 Preprocessor1_Mod…
 16     2   897     8 mcc     multiclass 0.3291    10 0.03298 Preprocessor1_Mod…
 17     2   406     9 mcc     multiclass 0.3288    10 0.03517 Preprocessor1_Mod…
 18     2   869     9 mcc     multiclass 0.3271    10 0.04418 Preprocessor1_Mod…
 19     2   785    11 mcc     multiclass 0.3267    10 0.02842 Preprocessor1_Mod…
 20     4   656     6 mcc     multiclass 0.3264    10 0.02781 Preprocessor1_Mod…
 21     2   753    12 mcc     multiclass 0.3256    10 0.03565 Preprocessor1_Mod…
 22     2  1451     8 mcc     multiclass 0.3247    10 0.03397 Preprocessor1_Mod…
 23     2  1328    11 mcc     multiclass 0.3238    10 0.03843 Preprocessor1_Mod…
 24     2  1439    15 mcc     multiclass 0.3236    10 0.04031 Preprocessor1_Mod…
 25     2   405    13 mcc     multiclass 0.3234    10 0.04489 Preprocessor1_Mod…
 26     2  1254    12 mcc     multiclass 0.3230    10 0.03477 Preprocessor1_Mod…
 27     4  1478    11 mcc     multiclass 0.3226    10 0.02975 Preprocessor1_Mod…
 28     2  1343     9 mcc     multiclass 0.3226    10 0.03234 Preprocessor1_Mod…
 29     2  1002    13 mcc     multiclass 0.3219    10 0.03661 Preprocessor1_Mod…
 30     2  1282    10 mcc     multiclass 0.3217    10 0.03545 Preprocessor1_Mod…
 31     3  1303    12 mcc     multiclass 0.3213    10 0.04008 Preprocessor1_Mod…
 32     3   451    14 mcc     multiclass 0.3209    10 0.03401 Preprocessor1_Mod…
 33     3   498    14 mcc     multiclass 0.3207    10 0.04239 Preprocessor1_Mod…
 34     4   835    16 mcc     multiclass 0.3199    10 0.03891 Preprocessor1_Mod…
 35     2   671    15 mcc     multiclass 0.3198    10 0.03597 Preprocessor1_Mod…
 36     2   933     6 mcc     multiclass 0.3196    10 0.03166 Preprocessor1_Mod…
 37     2   666    14 mcc     multiclass 0.3190    10 0.04368 Preprocessor1_Mod…
 38     2   517     6 mcc     multiclass 0.3188    10 0.03610 Preprocessor1_Mod…
 39     2  1139    18 mcc     multiclass 0.3183    10 0.03721 Preprocessor1_Mod…
 40     2  1213    13 mcc     multiclass 0.3183    10 0.04228 Preprocessor1_Mod…
 41     4   584    10 mcc     multiclass 0.3183    10 0.03494 Preprocessor1_Mod…
 42     2   464    17 mcc     multiclass 0.3182    10 0.03315 Preprocessor1_Mod…
 43     3   972    10 mcc     multiclass 0.3178    10 0.02655 Preprocessor1_Mod…
 44     2  1072    17 mcc     multiclass 0.3174    10 0.03000 Preprocessor1_Mod…
 45     2  1001    10 mcc     multiclass 0.3173    10 0.03172 Preprocessor1_Mod…
 46     2  1075    13 mcc     multiclass 0.3173    10 0.03821 Preprocessor1_Mod…
 47     2  1267    11 mcc     multiclass 0.3172    10 0.03371 Preprocessor1_Mod…
 48     2  1387    16 mcc     multiclass 0.3172    10 0.03837 Preprocessor1_Mod…
 49     3  1308     8 mcc     multiclass 0.3171    10 0.03106 Preprocessor1_Mod…
 50     3  1368     9 mcc     multiclass 0.3171    10 0.03165 Preprocessor1_Mod…
 51     2  1067    12 mcc     multiclass 0.3170    10 0.03623 Preprocessor1_Mod…
 52     3   595     7 mcc     multiclass 0.3169    10 0.02186 Preprocessor1_Mod…
 53     4  1077     9 mcc     multiclass 0.3168    10 0.03766 Preprocessor1_Mod…
 54     2  1048    17 mcc     multiclass 0.3167    10 0.03171 Preprocessor1_Mod…
 55     3  1445    13 mcc     multiclass 0.3167    10 0.03373 Preprocessor1_Mod…
 56     2   761    15 mcc     multiclass 0.3166    10 0.03119 Preprocessor1_Mod…
 57     2   717    21 mcc     multiclass 0.3161    10 0.02600 Preprocessor1_Mod…
 58     2   611    26 mcc     multiclass 0.3159    10 0.03864 Preprocessor1_Mod…
 59     2  1420     8 mcc     multiclass 0.3155    10 0.03015 Preprocessor1_Mod…
 60     4   409    14 mcc     multiclass 0.3152    10 0.04209 Preprocessor1_Mod…
 61     3  1414    16 mcc     multiclass 0.3150    10 0.03640 Preprocessor1_Mod…
 62     3   427    31 mcc     multiclass 0.3146    10 0.03684 Preprocessor1_Mod…
 63     3  1352    14 mcc     multiclass 0.3145    10 0.04424 Preprocessor1_Mod…
 64     4  1312    16 mcc     multiclass 0.3145    10 0.03978 Preprocessor1_Mod…
 65     3  1151    16 mcc     multiclass 0.3145    10 0.03953 Preprocessor1_Mod…
 66     3  1360    16 mcc     multiclass 0.3144    10 0.03963 Preprocessor1_Mod…
 67     3   681    16 mcc     multiclass 0.3144    10 0.03605 Preprocessor1_Mod…
 68     3  1448    17 mcc     multiclass 0.3144    10 0.04022 Preprocessor1_Mod…
 69     2  1092    40 mcc     multiclass 0.3140    10 0.03212 Preprocessor1_Mod…
 70     4  1388    18 mcc     multiclass 0.3139    10 0.03986 Preprocessor1_Mod…
 71     2  1401    35 mcc     multiclass 0.3139    10 0.03390 Preprocessor1_Mod…
 72     2   721     3 mcc     multiclass 0.3139    10 0.02970 Preprocessor1_Mod…
 73     3   971    20 mcc     multiclass 0.3138    10 0.03602 Preprocessor1_Mod…
 74     3   914    10 mcc     multiclass 0.3138    10 0.03626 Preprocessor1_Mod…
 75     2   506     5 mcc     multiclass 0.3137    10 0.02897 Preprocessor1_Mod…
 76     4   885     5 mcc     multiclass 0.3137    10 0.03220 Preprocessor1_Mod…
 77     3  1025    15 mcc     multiclass 0.3136    10 0.03868 Preprocessor1_Mod…
 78     3  1354    16 mcc     multiclass 0.3136    10 0.03879 Preprocessor1_Mod…
 79     2   995    32 mcc     multiclass 0.3136    10 0.03846 Preprocessor1_Mod…
 80     3   548     6 mcc     multiclass 0.3134    10 0.03161 Preprocessor1_Mod…
 81     3   424     5 mcc     multiclass 0.3134    10 0.03542 Preprocessor1_Mod…
 82     2   529    30 mcc     multiclass 0.3134    10 0.03325 Preprocessor1_Mod…
 83     2   421    19 mcc     multiclass 0.3134    10 0.03477 Preprocessor1_Mod…
 84     2  1076    15 mcc     multiclass 0.3133    10 0.03673 Preprocessor1_Mod…
 85     3   734    16 mcc     multiclass 0.3132    10 0.03867 Preprocessor1_Mod…
 86     2  1072    38 mcc     multiclass 0.3131    10 0.03145 Preprocessor1_Mod…
 87     4  1432    21 mcc     multiclass 0.3129    10 0.04111 Preprocessor1_Mod…
 88     3   762    15 mcc     multiclass 0.3128    10 0.04299 Preprocessor1_Mod…
 89     2  1084     5 mcc     multiclass 0.3128    10 0.02771 Preprocessor1_Mod…
 90     3   837    16 mcc     multiclass 0.3128    10 0.04240 Preprocessor1_Mod…
 91     2  1402    38 mcc     multiclass 0.3128    10 0.03303 Preprocessor1_Mod…
 92     2   598     6 mcc     multiclass 0.3126    10 0.02227 Preprocessor1_Mod…
 93     3   656     7 mcc     multiclass 0.3126    10 0.03318 Preprocessor1_Mod…
 94     2   774     6 mcc     multiclass 0.3125    10 0.03433 Preprocessor1_Mod…
 95     3  1157    11 mcc     multiclass 0.3125    10 0.03118 Preprocessor1_Mod…
 96     2   852     8 mcc     multiclass 0.3125    10 0.03635 Preprocessor1_Mod…
 97     2  1337    20 mcc     multiclass 0.3125    10 0.03760 Preprocessor1_Mod…
 98     3   581    27 mcc     multiclass 0.3124    10 0.03702 Preprocessor1_Mod…
 99     2   728    37 mcc     multiclass 0.3124    10 0.03351 Preprocessor1_Mod…
100     2  1371    21 mcc     multiclass 0.3124    10 0.03586 Preprocessor1_Mod…
101     2   985     7 mcc     multiclass 0.3123    10 0.02842 Preprocessor1_Mod…
102     3   687    30 mcc     multiclass 0.3121    10 0.03686 Preprocessor1_Mod…
103     3   591    15 mcc     multiclass 0.3121    10 0.03467 Preprocessor1_Mod…
104     2  1215     5 mcc     multiclass 0.3120    10 0.03177 Preprocessor1_Mod…
105     2  1296    21 mcc     multiclass 0.3120    10 0.03633 Preprocessor1_Mod…
106     2   690    15 mcc     multiclass 0.3120    10 0.03889 Preprocessor1_Mod…
107     3   814    11 mcc     multiclass 0.3119    10 0.02750 Preprocessor1_Mod…
108     2   981     6 mcc     multiclass 0.3117    10 0.02862 Preprocessor1_Mod…
109     3  1016     8 mcc     multiclass 0.3117    10 0.03228 Preprocessor1_Mod…
110     2  1080    12 mcc     multiclass 0.3117    10 0.04353 Preprocessor1_Mod…
111     4  1441    13 mcc     multiclass 0.3116    10 0.04118 Preprocessor1_Mod…
112     3   499     4 mcc     multiclass 0.3115    10 0.03353 Preprocessor1_Mod…
113     2  1278    11 mcc     multiclass 0.3113    10 0.04034 Preprocessor1_Mod…
114     2   732     7 mcc     multiclass 0.3113    10 0.03873 Preprocessor1_Mod…
115     3  1497    13 mcc     multiclass 0.3113    10 0.03790 Preprocessor1_Mod…
116     2   551     5 mcc     multiclass 0.3111    10 0.02934 Preprocessor1_Mod…
117     2  1445    23 mcc     multiclass 0.3111    10 0.02657 Preprocessor1_Mod…
118     4  1016     9 mcc     multiclass 0.3111    10 0.03258 Preprocessor1_Mod…
119     2  1254     6 mcc     multiclass 0.3109    10 0.02959 Preprocessor1_Mod…
120     3  1346    10 mcc     multiclass 0.3108    10 0.03143 Preprocessor1_Mod…
121     4  1129     9 mcc     multiclass 0.3107    10 0.03218 Preprocessor1_Mod…
122     2   671    21 mcc     multiclass 0.3107    10 0.03821 Preprocessor1_Mod…
123     2  1040    10 mcc     multiclass 0.3107    10 0.03108 Preprocessor1_Mod…
124     2  1102    18 mcc     multiclass 0.3104    10 0.03337 Preprocessor1_Mod…
125     2   746     7 mcc     multiclass 0.3104    10 0.03091 Preprocessor1_Mod…
126     4  1287    14 mcc     multiclass 0.3103    10 0.03517 Preprocessor1_Mod…
127     3  1034    16 mcc     multiclass 0.3102    10 0.03489 Preprocessor1_Mod…
128     3   899    11 mcc     multiclass 0.3102    10 0.03494 Preprocessor1_Mod…
129     2   506    11 mcc     multiclass 0.3099    10 0.02919 Preprocessor1_Mod…
130     2   569     7 mcc     multiclass 0.3098    10 0.03781 Preprocessor1_Mod…
131     3  1390    11 mcc     multiclass 0.3098    10 0.02918 Preprocessor1_Mod…
132     2   457    29 mcc     multiclass 0.3098    10 0.03723 Preprocessor1_Mod…
133     2   695     6 mcc     multiclass 0.3097    10 0.02688 Preprocessor1_Mod…
134     3  1272    11 mcc     multiclass 0.3096    10 0.03752 Preprocessor1_Mod…
135     2  1225    10 mcc     multiclass 0.3095    10 0.04009 Preprocessor1_Mod…
136     3  1445    17 mcc     multiclass 0.3095    10 0.04398 Preprocessor1_Mod…
137     3  1339    34 mcc     multiclass 0.3094    10 0.03321 Preprocessor1_Mod…
138     2   766    35 mcc     multiclass 0.3094    10 0.03718 Preprocessor1_Mod…
139     2  1361    15 mcc     multiclass 0.3094    10 0.03837 Preprocessor1_Mod…
140     2  1450    13 mcc     multiclass 0.3093    10 0.03581 Preprocessor1_Mod…
141     2   896    12 mcc     multiclass 0.3092    10 0.03647 Preprocessor1_Mod…
142     2  1237    18 mcc     multiclass 0.3089    10 0.03841 Preprocessor1_Mod…
143     2   733     8 mcc     multiclass 0.3089    10 0.02588 Preprocessor1_Mod…
144     4  1213    10 mcc     multiclass 0.3087    10 0.03544 Preprocessor1_Mod…
145     2   469     8 mcc     multiclass 0.3084    10 0.03818 Preprocessor1_Mod…
146     4  1438     7 mcc     multiclass 0.3084    10 0.02845 Preprocessor1_Mod…
147     2   984    21 mcc     multiclass 0.3082    10 0.03432 Preprocessor1_Mod…
148     3   774    17 mcc     multiclass 0.3082    10 0.03442 Preprocessor1_Mod…
149     3   794    18 mcc     multiclass 0.3081    10 0.04100 Preprocessor1_Mod…
150     3  1483    15 mcc     multiclass 0.3080    10 0.04115 Preprocessor1_Mod…
151     4   957    20 mcc     multiclass 0.3079    10 0.03851 Preprocessor1_Mod…
152     3  1135    16 mcc     multiclass 0.3078    10 0.04449 Preprocessor1_Mod…
153     3   721     3 mcc     multiclass 0.3077    10 0.03140 Preprocessor1_Mod…
154     4   915    14 mcc     multiclass 0.3077    10 0.03800 Preprocessor1_Mod…
155     2  1306    40 mcc     multiclass 0.3077    10 0.03152 Preprocessor1_Mod…
156     2   640    33 mcc     multiclass 0.3076    10 0.03017 Preprocessor1_Mod…
157     2   470     6 mcc     multiclass 0.3075    10 0.03040 Preprocessor1_Mod…
158     3   645     6 mcc     multiclass 0.3075    10 0.03300 Preprocessor1_Mod…
159     2  1009    30 mcc     multiclass 0.3075    10 0.03618 Preprocessor1_Mod…
160     4  1293     7 mcc     multiclass 0.3072    10 0.02954 Preprocessor1_Mod…
161     4  1270    18 mcc     multiclass 0.3072    10 0.04257 Preprocessor1_Mod…
162     2   548    17 mcc     multiclass 0.3072    10 0.03215 Preprocessor1_Mod…
163     3   683    18 mcc     multiclass 0.3071    10 0.03701 Preprocessor1_Mod…
164     2   753    10 mcc     multiclass 0.3070    10 0.03075 Preprocessor1_Mod…
165     2  1401    17 mcc     multiclass 0.3069    10 0.03939 Preprocessor1_Mod…
166     2   755    27 mcc     multiclass 0.3068    10 0.03377 Preprocessor1_Mod…
167     2   834    30 mcc     multiclass 0.3068    10 0.02876 Preprocessor1_Mod…
168     3  1350    28 mcc     multiclass 0.3067    10 0.03829 Preprocessor1_Mod…
169     3  1104    22 mcc     multiclass 0.3067    10 0.03553 Preprocessor1_Mod…
170     4   774    27 mcc     multiclass 0.3066    10 0.03882 Preprocessor1_Mod…
171     3   475     6 mcc     multiclass 0.3066    10 0.02778 Preprocessor1_Mod…
172     3   949     6 mcc     multiclass 0.3066    10 0.03588 Preprocessor1_Mod…
173     4  1281     5 mcc     multiclass 0.3066    10 0.03175 Preprocessor1_Mod…
174     3   403     7 mcc     multiclass 0.3064    10 0.02664 Preprocessor1_Mod…
175     3   660    13 mcc     multiclass 0.3064    10 0.03780 Preprocessor1_Mod…
176     3   842    16 mcc     multiclass 0.3064    10 0.04167 Preprocessor1_Mod…
177     3   503    12 mcc     multiclass 0.3063    10 0.02761 Preprocessor1_Mod…
178     3   681    18 mcc     multiclass 0.3063    10 0.04423 Preprocessor1_Mod…
179     2  1469    14 mcc     multiclass 0.3062    10 0.03093 Preprocessor1_Mod…
180     4   564     8 mcc     multiclass 0.3062    10 0.02303 Preprocessor1_Mod…
181     3   779    19 mcc     multiclass 0.3062    10 0.04245 Preprocessor1_Mod…
182     4   586     8 mcc     multiclass 0.3061    10 0.03330 Preprocessor1_Mod…
183     2   616    14 mcc     multiclass 0.3060    10 0.04206 Preprocessor1_Mod…
184     3  1451    12 mcc     multiclass 0.3059    10 0.03616 Preprocessor1_Mod…
185     2   420    15 mcc     multiclass 0.3059    10 0.04279 Preprocessor1_Mod…
186     4  1397    15 mcc     multiclass 0.3059    10 0.03585 Preprocessor1_Mod…
187     4   551    13 mcc     multiclass 0.3058    10 0.03140 Preprocessor1_Mod…
188     2  1468    13 mcc     multiclass 0.3058    10 0.03164 Preprocessor1_Mod…
189     3  1475    12 mcc     multiclass 0.3058    10 0.03583 Preprocessor1_Mod…
190     2   525    18 mcc     multiclass 0.3057    10 0.02869 Preprocessor1_Mod…
191     2   688    39 mcc     multiclass 0.3057    10 0.03691 Preprocessor1_Mod…
192     3   490    21 mcc     multiclass 0.3057    10 0.03643 Preprocessor1_Mod…
193     2  1307    22 mcc     multiclass 0.3056    10 0.03661 Preprocessor1_Mod…
194     2  1226     5 mcc     multiclass 0.3055    10 0.03152 Preprocessor1_Mod…
195     2   504     4 mcc     multiclass 0.3054    10 0.03263 Preprocessor1_Mod…
196     2   667    13 mcc     multiclass 0.3054    10 0.03957 Preprocessor1_Mod…
197     4   800    18 mcc     multiclass 0.3053    10 0.03248 Preprocessor1_Mod…
198     2  1330    17 mcc     multiclass 0.3053    10 0.03179 Preprocessor1_Mod…
199     4  1336    17 mcc     multiclass 0.3052    10 0.03320 Preprocessor1_Mod…
200     4   794    24 mcc     multiclass 0.3052    10 0.03660 Preprocessor1_Mod…

Efikasnost

Column 1

Metrike na testnom i trening skupu

# A tibble: 9 × 4
  .metric     .estimator trening   test
  <chr>       <chr>        <dbl>  <dbl>
1 sens        macro       1      0.4366
2 precision   macro       1      0.4253
3 spec        macro       1      0.8256
4 accuracy    multiclass  1      0.5059
5 f_meas      macro       1      0.4116
6 mcc         multiclass  1      0.3112
7 kap         multiclass  1      0.3012
8 roc_auc     hand_till   1      0.7722
9 mn_log_loss multiclass  0.2690 1.039

Confusion matrix

Column 2

ROC curve

Gain curve

Važnost prediktora

Columnn 1

Gini

permutacija

Column 2

Boruta

Boruta (history)

---
title: "MAT2 chatGPT"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    source_code: embed
---

```{css, echo=FALSE}
.sidebar { overflow: auto; }
.dataTables_scrollBody {
    height:95% !important;
    max-height:95% !important;
}
.chart-stage-flex {
    overflow:auto !important;
}
```

```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(readxl)
library(corrplot)
library(knitr)
library(kableExtra)
library(naniar)
library(bnlearn)
library(Rgraphviz)
library(gRain)
library(ggsankey)
library(tidymodels)
library(Boruta)
library(vip)
options(pillar.sigfig = 4)

studenti_RED_minus1 <- c("bcovic22@student.foi.hr",
                         "lkronast22@student.foi.hr",
                         "khozjan22@student.foi.hr",
                         "mkelemen22@student.foi.hr",
                         "dkurochki22@student.foi.hr")

raz <- read_excel("MAT2_2022_2023_RAZ.xlsx") %>% 
  select(mail)

izv <- read_excel("MAT2_2022_2023_IZV.xlsx") %>% 
  select(mail)

podaci <- read_excel("MAT2_2022_2023.xlsx") %>%
  mutate(Status = case_when(mail %in% izv$mail ~ "IZV",
                            mail %in% raz$mail ~ "RAZ",
                            .default = "RED"),
         OCJENA = case_when(mail %in% studenti_RED_minus1 ~ -1,
                            is.na(OCJENA) | OCJENA < 20 ~ 0,
                            OCJENA < 50 ~ 1,
                            OCJENA < 61 ~ 2,
                            OCJENA < 76 ~ 3,
                            OCJENA < 91 ~ 4,
                            .default = 5)) %>%
  mutate(DZ = rowSums(across(DZ1:DZ0), na.rm = TRUE), 
         KVIZ = DZ_KVIZ - DZ,
         ESEJ = ifelse(ESEJ == 0, NA, ESEJ)) %>%
  relocate(Status, .after = Prezime) %>%
  relocate(c(DZ:KVIZ, ESEJ:ANKETA), .before = KOL1) %>%
  select(Ime:KOL, USMENI, OCJENA) %>%
  mutate(UKUPNO = case_when(Status == "RED" ~ rowSums(across(DZ:KOL3), na.rm = TRUE),
                            .default = rowSums(across(ESEJ:KOL3), na.rm = TRUE))) %>%
  relocate(OCJENA, .after = last_col())

MAT2_KOL_redovni <- podaci %>% filter(Status == "RED", OCJENA != -1) %>%
  unite("student", Ime, Prezime, sep = " ") %>%
  select(c(student, KOL1:KOL3, DZ, KVIZ, ESEJ)) %>%
  pivot_longer(c(KOL1:KOL3, DZ, KVIZ, ESEJ), names_to = "aktivnost", values_to = "bodovi") %>%
  mutate(aktivnost = factor(aktivnost, levels=c('KOL1', 'KOL2', 'KOL3', 'KVIZ', 'DZ', 'ESEJ')))

korelacije_MAT2 <- podaci %>% filter(Status == "RED", OCJENA != -1) %>% 
  select(c(KOL1, KOL2, KOL3, KVIZ, DZ, ESEJ))
testRes_MAT2 <- cor.mtest(korelacije_MAT2, conf.level = 0.975)
 
AS_MED_MAT2 <- MAT2_KOL_redovni %>% group_by(aktivnost) %>% 
   summarise(mean_value = mean(bodovi,na.rm=TRUE), median_value = median(bodovi,na.rm=TRUE))
 
MAT2_text <- data.frame(
  label_mean = rep("mean", 6),
  label_median = rep("median", 6),
  aktivnost = factor(c("KOL1", "KOL2", "KOL3", "KVIZ", "DZ", "ESEJ")),
  x_pos = c(18, 18, 18, 18, 9.25, 9.25),
  bullet_pos = c(17.5, 17.5, 17.5, 17.5, 9, 9)
)

MAT2_blank <- data.frame(
  aktivnost = factor(rep(c("KOL1", "KOL2", "KOL3", "KVIZ", "DZ", "ESEJ"), each=2)),
  x = c(0,20,0,20,0,20,0,20,0,10,0,10),
  y = 0
)

MAT2_usmeni_pad <- podaci %>% filter(str_detect(USMENI, "^Nije")) %>%
  unite("student", Ime, Prezime, sep = " ") %>%
  select(c(student, KOL1:KOL3, DZ, KVIZ, ESEJ)) %>%
  pivot_longer(c(KOL1:KOL3, DZ, KVIZ, ESEJ), names_to = "aktivnost", values_to = "bodovi") %>%
  mutate(aktivnost = factor(aktivnost, levels=c('KOL1', 'KOL2', 'KOL3', 'KVIZ', 'DZ', 'ESEJ')))

MAT2_usmeni_prolaz <- podaci %>% filter(str_detect(USMENI, "^Zad")) %>%
  unite("student", Ime, Prezime, sep = " ") %>%
  select(c(student, KOL1:KOL3, DZ, KVIZ, ESEJ)) %>%
  pivot_longer(c(KOL1:KOL3, DZ, KVIZ, ESEJ), names_to = "aktivnost", values_to = "bodovi") %>%
  mutate(aktivnost = factor(aktivnost, levels=c('KOL1', 'KOL2', 'KOL3', 'KVIZ', 'DZ', 'ESEJ')))

# samo redovni studenti (ukupno 334)
podaci_redovni <- podaci %>% filter(Status == "RED", OCJENA != -1) %>%
  mutate_if(is.numeric, ~replace_na(., 0))
# redovni koji su pisali esej
podaci_redovni_esej <- podaci_redovni %>% filter(ESEJ != 0)

podaci_ESEJ <-podaci_redovni %>%
  mutate(Kategorija = case_when(
    UKUPNO < 25 ~ "0 \U2264 UKUPNO < 25",
    UKUPNO < 50 ~ "25 \U2264 UKUPNO < 50",
    UKUPNO < 75 ~ "50 \U2264 UKUPNO < 75",
    .default = "75 \U2264 UKUPNO \U2264 100"),
    Kategorija = factor(Kategorija, 
                        levels = c("0 \U2264 UKUPNO < 25", "25 \U2264 UKUPNO < 50",
                                   "50 \U2264 UKUPNO < 75", "75 \U2264 UKUPNO \U2264 100"))) %>% 
  select(Ime, Prezime, mail, ESEJ, Kategorija)

qvrt <- quantile(podaci_redovni$UKUPNO, probs = seq(0, 1, 0.25))
qvrt_esej <- quantile(podaci_redovni_esej$UKUPNO, probs = seq(0, 1, 0.25))

podaci_ESEJ_kvartili <- podaci_redovni %>%
  mutate(Kategorija = case_when(
    UKUPNO < qvrt['25%'] ~ paste(qvrt['0%'], "\U2264 UKUPNO <", qvrt['25%']),
    UKUPNO < qvrt['50%'] ~ paste(qvrt['25%'], "\U2264 UKUPNO <", qvrt['50%']),
    UKUPNO < qvrt['75%'] ~ paste(qvrt['50%'], "\U2264 UKUPNO <", qvrt['75%']),
    .default = paste(qvrt['75%'], "\U2264 UKUPNO \U2264", qvrt['100%'])),
    Kategorija = factor(Kategorija, 
                        levels = c(paste(qvrt['0%'], "\U2264 UKUPNO <", qvrt['25%']), 
                                   paste(qvrt['25%'], "\U2264 UKUPNO <", qvrt['50%']),
                                   paste(qvrt['50%'], "\U2264 UKUPNO <", qvrt['75%']), 
                                   paste(qvrt['75%'], "\U2264 UKUPNO \U2264", qvrt['100%'])))) %>% 
  select(Ime, Prezime, mail, ESEJ, Kategorija)

podaci_pisaliESEJ_kvartili <- podaci_redovni_esej %>%
  mutate(Kategorija = case_when(
    UKUPNO < qvrt_esej['25%'] ~ paste(qvrt_esej['0%'], "\U2264 UKUPNO <", qvrt_esej['25%']),
    UKUPNO < qvrt_esej['50%'] ~ paste(qvrt_esej['25%'], "\U2264 UKUPNO <", qvrt_esej['50%']),
    UKUPNO < qvrt_esej['75%'] ~ paste(qvrt_esej['50%'], "\U2264 UKUPNO <", qvrt_esej['75%']),
    .default = paste(qvrt_esej['75%'], "\U2264 UKUPNO \U2264", qvrt_esej['100%'])),
    Kategorija = factor(Kategorija, 
                        levels = c(paste(qvrt_esej['0%'], "\U2264 UKUPNO <", qvrt_esej['25%']), 
                                   paste(qvrt_esej['25%'], "\U2264 UKUPNO <", qvrt_esej['50%']),
                                   paste(qvrt_esej['50%'], "\U2264 UKUPNO <", qvrt_esej['75%']), 
                                   paste(qvrt_esej['75%'], "\U2264 UKUPNO \U2264", qvrt_esej['100%'])))) %>% 
  select(Ime, Prezime, mail, ESEJ, Kategorija)

kviz <- read_csv("MAT2_2022_2023_kvizovi.csv") %>%
  right_join(podaci_ESEJ %>% select(mail, Kategorija), by = "mail")

esej_kategorije <- podaci_ESEJ %>% group_by(Kategorija) %>%
  summarize(`broj studenata` = n(), `pisalo esej` = sum(ESEJ != 0))

nije_pisalo_esej <- podaci_ESEJ %>% count(ESEJ) %>% 
  filter(ESEJ == 0) %>% pull(n)
pisalo_esej <- sum(esej_kategorije$`pisalo esej`)

esej_kategorije_kvartili <- podaci_ESEJ_kvartili %>% group_by(Kategorija) %>%
  summarize(`broj studenata` = n(), `pisalo esej` = sum(ESEJ != 0))

pisali_esej_kategorije_kvartili <- podaci_pisaliESEJ_kvartili %>% group_by(Kategorija) %>%
  summarize(`broj studenata` = n())

kviz_kategorije <- kviz %>% pivot_longer(DER:K13, names_to = "KVIZ", values_to = "value") %>%
  select(Kategorija, KVIZ, value) %>%
  mutate(KVIZ = fct_relevel(factor(KVIZ), c("K10", "K11", "K12", "K13"), after = Inf))

# KVIZ1 - max 7 bodova, KVIZ2 - max 8 bodova, KVIZ3 - max 5 bodova
bayes_podaci <- podaci_redovni %>% left_join(kviz, by = "mail") %>%
  mutate(KVIZ1 = rowSums(across(K1:K4), na.rm = TRUE), 
         KVIZ2 = rowSums(across(K5:K9), na.rm = TRUE),
         KVIZ3 = rowSums(across(K10:K13), na.rm = TRUE)) %>%
  select(KOL1, KOL2, KOL3, ESEJ, KVIZ1, KVIZ2, KVIZ3) %>%
  replace(is.na(.), 0)

bayes_klase <- bayes_podaci %>%
  mutate(KOL1 = case_when(KOL1 <= 5 ~ "1", 
                          KOL1 <= 10 ~ "2",
                          KOL1 <= 15 ~ "3",
                          .default = "4"),
         KOL2 = case_when(KOL2 <= 5 ~ "1", 
                          KOL2 <= 10 ~ "2",
                          KOL2 <= 15 ~ "3",
                          .default = "4"),
         KOL3 = case_when(KOL3 <= 5 ~ "1",
                          KOL3 <= 10 ~ "2",
                          KOL3 <= 15 ~ "3",
                          .default = "4"),
         ESEJ = case_when(ESEJ <= 2.5 ~ "1", 
                          ESEJ <= 5 ~ "2",
                          ESEJ <= 7.5 ~ "3",
                          .default = "4"),
         KVIZ1 = case_when(KVIZ1 <= 1.75 ~ "1", 
                           KVIZ1 <= 3.5 ~ "2",
                           KVIZ1 <= 5.25 ~ "3",
                           .default = "4"),
         KVIZ2 = case_when(KVIZ2 <= 2 ~ "1", 
                           KVIZ2 <= 4 ~ "2",
                           KVIZ2 <= 6 ~ "3",
                           .default = "4"),
         KVIZ3 = case_when(KVIZ3 <= 1.25 ~ "1", 
                           KVIZ3 <= 2.5 ~ "2",
                           KVIZ3 <= 3.75 ~ "3",
                           .default = "4")) %>%
  mutate_all(~fct_relevel(., c("1","2","3","4")))

bayes_klase <- as.data.frame(bayes_klase)

learn1 <- readRDS("learn1.rds")
learn2 <- readRDS("learn2.rds")
learn3 <- readRDS("learn3.rds")
dag3 <- readRDS("dag3.rds")

bn1 <- readRDS("bn1.rds")
bn2 <- readRDS("bn2.rds")
bn3 <- readRDS("bn3.rds")

bayes_klase_long <- bayes_klase %>% 
  make_long(KVIZ1, KOL1, KVIZ2, KOL2, ESEJ, KVIZ3, KOL3)

postoci <- bayes_klase_long %>% group_by(x, node) %>% summarize(n = n()) %>% 
  ungroup(node) %>% mutate(pct2 = n / sum(n) * 100, pct = round(pct2))
postoci[9,"pct"] <- 50
postoci[20,"pct"] <- 15

bayes_klase_long2 <- bayes_klase_long %>% left_join(postoci, by = c("x","node"))

mat1 <- readRDS("mat1.rds")
mat1_tr <- readRDS("mat1_tr.rds")
mat2 <- readRDS("mat2.rds")
mat2_tr <- readRDS("mat2_tr.rds")
mat3 <- readRDS("mat3.rds")
mat3_tr <- readRDS("mat3_tr.rds")

rf_metrike <- metric_set(roc_auc, sens, precision, spec, accuracy, f_meas, mcc, kap, mn_log_loss)
update_geom_defaults(geom = "tile", new = list(color = "black"))

RF_fit <- readRDS("RF_fit.rds")
RF_tuning <- readRDS("RF_tuning.rds")
RF_work <- readRDS("RF_work.rds")
RF_fit_perm <- readRDS("RF_fit_perm.rds")
RF_work_perm <- readRDS("RF_work_perm.rds")
RF_Boruta <- readRDS("RF_Boruta.rds")
```

Popisi {data-navmenu="Sumarne analize"}
=======================================================================

Column {.tabset .tabset-fade}
-----------------------------------------------------------------------

### Svi studenti

```{r}
podaci %>% arrange(desc(Status), desc(UKUPNO), desc(KOL)) %>%
  mutate(id=row_number()) %>%
  relocate(id) %>%
  kbl(caption = "Svi studenti") %>%
  kable_classic("hover",full_width = F, html_font = "Cambria")
```

### Kolokvirali

```{r}
podaci %>% filter(OCJENA > 1) %>% 
  arrange(desc(Status), desc(UKUPNO), desc(KOL)) %>%
  mutate(id=row_number()) %>%
  relocate(id) %>%
  kbl(caption = "Studenti koji su kolokvirali") %>%
  kable_classic("hover",full_width = F, html_font = "Cambria")
```

### Pad na usmenom

```{r}
podaci %>% filter(str_detect(USMENI, "^Nije")) %>% 
  arrange(desc(Status), desc(UKUPNO), desc(KOL)) %>%
  mutate(id=row_number()) %>%
  relocate(id) %>%
  kbl(caption = "Studenti koji su pali na usmenom ispitu") %>%
  kable_classic("hover",full_width = F, html_font = "Cambria")
```

Gustoće {data-navmenu="Sumarne analize"}
=======================================================================

Column {data-width=500}
-----------------------------------------------------------------------

### Funkcije gustoća po aktivnostima (redovni studenti)

```{r warning=FALSE, fig.width=17, fig.height=9}
ggplot(MAT2_KOL_redovni, aes(x=bodovi)) + 
  geom_histogram(aes(y=..density..), binwidth = 1, boundary=1, 
                 color="#ec8ae5", fill="#48d09b", alpha=0.7) +
  geom_density(alpha=.2, fill="yellow") + geom_blank(data=MAT2_blank, aes(x=x,y=y)) +
  scale_x_continuous(name = "bodovi") + 
  scale_y_continuous(limits = c(0, 0.25)) + guides(color = "none", fill = "none") +
  facet_wrap(vars(aktivnost), scales = "free_x") +
  geom_vline(data=AS_MED_MAT2, aes(xintercept=mean_value), color="blue", 
             linetype="dashed", size=0.5, alpha=0.6) +
  geom_vline(data=AS_MED_MAT2, aes(xintercept=median_value), color="red", 
             linetype="dashed", size=0.5, alpha=0.6) +
  geom_text(data = MAT2_text, mapping = aes(label = "\u2022", x = bullet_pos), 
            y = 0.24, color = "blue", size = 9) + 
  geom_text(data = MAT2_text, mapping = aes(label = label_mean, x = x_pos), 
            y = 0.24, hjust = 0) +
  geom_text(data = MAT2_text, mapping = aes(label = "\u2022", x = bullet_pos), 
            y = 0.22, color = "red", size = 9) +
  geom_text(data = MAT2_text, mapping = aes(label = label_median, x = x_pos), 
            y = 0.22, hjust = 0) +
  geom_rug(alpha=0.3)
```

Column {data-width=150}
-----------------------------------------------------------------------

### Broj studenata po statusu {data-height=100}

```{r warning=FALSE}
podaci %>% filter(OCJENA != -1) %>% 
  count(Status) %>% kbl() %>% kable_styling()
```

### Brojčane vrijednosti (redovni studenti) {data-height=200}

```{r warning=FALSE}
MAT2_KOL_redovni %>% group_by(aktivnost) %>%
  summarize(BROJ = sum(!is.na(bodovi)),
            MEAN = round(mean(bodovi, na.rm = TRUE), 2),
            MEDIAN = median(bodovi, na.rm = TRUE), 
            MIN = min(bodovi, na.rm = TRUE),
            MAX = max(bodovi, na.rm = TRUE)) %>%
  kbl() %>% kable_styling()
```


Violine {data-navmenu="Sumarne analize"}
=======================================================================

```{r warning=FALSE, fig.width=17, fig.height=9}
ggplot(MAT2_KOL_redovni, aes(x=aktivnost, y=bodovi, fill=aktivnost)) +
  geom_dotplot(binaxis='y', stackdir='center', fill='black', alpha=0.3, dostsize = 0.2) +
  geom_violin(trim=F, alpha=0.3) + geom_boxplot(width=0.05, alpha=0.8) + xlab('') +
  theme(axis.text.x=element_text(size=11), axis.text.y=element_text(size=11), legend.position='none') +
  facet_wrap(vars(aktivnost), scale = "free") +
  theme(axis.text.x=element_blank(), axis.ticks.x=element_blank())
```

Ukupni bodovi {data-navmenu="Sumarne analize"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Frekvencije ukupnih bodova (redovni studenti)

```{r warning=FALSE, fig.width=10, fig.height=5}
ggplot(podaci %>% filter(Status == "RED", OCJENA != -1), aes(x=UKUPNO)) +
  geom_histogram(binwidth = 1, boundary=1, color="#ec8ae5", fill="#48d09b", alpha=0.7) +
  scale_x_continuous(name = "ukupni bodovi", breaks = seq(0,100,5), limits = c(0,100)) +
  scale_y_continuous(name = "broj studenata", breaks = seq(0,30,2)) +
  theme(panel.grid.minor = element_blank())
```

### Ocjene (redovni)

```{r warning=FALSE, fig.width=10, fig.height=5}
ggplot(podaci %>% filter(Status == "RED", OCJENA != -1), aes(x=factor(OCJENA))) +
  geom_bar(width=0.7, fill="steelblue") +
  geom_text(stat="count", aes(label=..count..), vjust=-0.5, nudge_x = -0.2, size = 5) +
  geom_text(aes( label = sprintf('(%s)', scales::percent(..prop..)), group = 1), stat= "count",
            vjust = -.5, nudge_x = 0.15, size = 5) +
  scale_x_discrete(name = "Ocjena") +
  scale_y_continuous(name = "broj studenata", breaks = seq(0,250,25), limits = c(0,250)) +
  theme(panel.grid.major.x = element_blank())
```

Column {data-width=400}
-----------------------------------------------------------------------

### Distribucija bodova

```{r warning=FALSE, fig.width=10, fig.height=7}
ggplot(podaci %>% filter(Status == "RED", OCJENA != -1), aes(x=UKUPNO)) +
  geom_histogram(aes(y=..density..), color="#ec8ae5", fill="#48d09b", alpha=0.5,
                 breaks = c(0,20,50,61,75,91,100)) +
  geom_vline(aes(xintercept=mean(UKUPNO)),color="blue", linetype="dashed", size=0.5, alpha=0.6) +
  geom_vline(aes(xintercept=median(UKUPNO)),color="red", linetype="dashed", size=0.5, alpha=0.6) +
  annotate(geom="point", x=90, y=0.035, size=2, shape=21, fill="red",color="red") +
  annotate(geom="text", x=91, y=0.035, label="median",size=3.5,hjust=0) +
  annotate(geom="point", x=90, y=0.033, size=2, shape=21, fill="blue",color="blue") +
  annotate(geom="text", x=91, y=0.033, label="mean",size=3.5,hjust=0) +
  geom_density(alpha=.2, fill="yellow") +
  geom_rug(alpha=0.3) +
  scale_x_continuous(name = "ukupni bodovi", breaks = seq(0,100,5), limits = c(0,100)) +
  theme(panel.grid.minor.x = element_blank())
```

Korelacije {data-navmenu="Sumarne analize"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Korelacije

```{r warning=FALSE, fig.width=10, fig.height=7}
corrplot(round(cor(korelacije_MAT2, use = "pairwise.complete.obs"), 2), type = "lower",
         diag=FALSE, addCoef.col = 'black', tl.srt = 45)
```

Column {data-width=400}
-----------------------------------------------------------------------

### p-vrijednosti korelacija

```{r warning=FALSE, fig.width=10, fig.height=7}
corrplot(round(cor(korelacije_MAT2, use = "pairwise.complete.obs"), 2), type = "lower",
         diag=FALSE, tl.srt = 45, p.mat = testRes_MAT2$p, insig = 'p-value', sig.level = -1)
```

Usmeni - grafički prikaz  {data-navmenu="Analiza usmenog"}
=======================================================================

### Usmeni (grafički prikaz)

```{r fig.height = 6}
ggplot(podaci %>% filter(!is.na(USMENI)), aes(x = factor(USMENI))) +
  geom_bar(fill="steelblue") +
  geom_text(stat="count", aes(label=..count..), vjust=-0.5, nudge_x = -0.1, size = 5) +
  geom_text(aes( label = sprintf('(%s)', scales::percent(..prop..)), group = 1), stat= "count",
            vjust = -.5, nudge_x = 0.1, size = 5) +
  xlab("") + ylab("") +
  scale_y_continuous(breaks = seq(0,100,25), limits = c(0,105)) 
```

Pad i prolaz na usmenom {data-navmenu="Analiza usmenog"}
=======================================================================

## Column 1

### Frekvencije bodova po aktivnostima studenata koji su pali na usmenom

```{r warning=FALSE}
ggplot(MAT2_usmeni_pad, aes(x=bodovi)) + 
  geom_histogram(binwidth = 1, boundary=1, color="#ec8ae5", fill="#48d09b", alpha=0.7) +
  geom_blank(data=MAT2_blank, aes(x=x,y=y)) +
  facet_wrap(vars(aktivnost), scale = "free") + xlab("") + ylab("")
```

## Column 2

### Frekvencije bodova po aktivnostima studenata koji su prošli na usmenom

```{r warning=FALSE}
ggplot(MAT2_usmeni_prolaz, aes(x=bodovi)) + 
  geom_histogram(binwidth = 1, boundary=1, color="#ec8ae5", fill="#48d09b", alpha=0.7) +
  geom_blank(data=MAT2_blank, aes(x=x,y=y)) +
  facet_wrap(vars(aktivnost), scale = "free") + xlab("") + ylab("")
```

Esej po kategorijama {data-navmenu="Dodatne analize"}
=======================================================================

## Column 1 {.tabset .tabset-fade data-width=200}

### četvrtine

```{r warning=FALSE, fig.width=8}
ggplot(podaci_ESEJ, aes(x=ESEJ)) + 
  geom_histogram(binwidth = 1, boundary=1, color="#ec8ae5", fill="#48d09b", alpha=0.7) +
  scale_x_continuous(breaks = seq(0,10,1), limits = c(0,10)) +
  facet_wrap(vars(Kategorija)) +
  labs(caption = "kategorije su napravljene prema ukupnim bodovima redovnih studenata")
```

### kvartili 1

```{r warning=FALSE, fig.width=8}
ggplot(podaci_ESEJ_kvartili, aes(x=ESEJ)) + 
  geom_histogram(binwidth = 1, boundary=1, color="#ec8ae5", fill="#48d09b", alpha=0.7) +
  scale_x_continuous(breaks = seq(0,10,1), limits = c(0,10)) +
  facet_wrap(vars(Kategorija)) +
  labs(caption = "kategorije su napravljene prema kvartilima ukupnih bodova redovnih studenata")
```

### kvartili 2

```{r warning=FALSE, fig.width=8}
ggplot(podaci_pisaliESEJ_kvartili, aes(x=ESEJ)) + 
  geom_histogram(binwidth = 1, boundary=1, color="#ec8ae5", fill="#48d09b", alpha=0.7) +
  scale_x_continuous(breaks = seq(0,10,1), limits = c(0,10)) +
  facet_wrap(vars(Kategorija)) +
  labs(caption = "kategorije su napravljene prema kvartilima ukupnih bodova redovnih studenata koji su pisali esej")
```

## Column 2 {.tabset .tabset-fade data-width=70}

### Kategorije

```{r warning=FALSE}
esej_kategorije %>% 
  kbl(caption = "Broj redovnih studenata koji su pisali esej (četvrtine)") %>% 
  kable_styling()
```

```{r warning=FALSE}
esej_kategorije_kvartili %>% 
  kbl(caption = "Broj redovnih studenata koji su pisali esej (kvartili 1)") %>% 
  kable_styling()
```

```{r warning=FALSE}
pisali_esej_kategorije_kvartili %>% 
  kbl(caption = "Broj redovnih studenata koji su pisali esej (kvartili 2)") %>% 
  kable_styling()
```

### Ukupno

```{r warning=FALSE}
tibble(`pisalo esej` = pisalo_esej, 
       `nije pisalo esej` = nije_pisalo_esej) %>% 
  kbl(caption = "Esej (ukupno u sumi za redovne studente)") %>% kable_styling()
```

```{r warning=FALSE}
tibble(`pisalo esej` = scales::percent(round(pisalo_esej / (pisalo_esej + nije_pisalo_esej),2)), 
       `nije pisalo esej` = scales::percent(round(nije_pisalo_esej / (pisalo_esej + nije_pisalo_esej),2))) %>% 
  kbl(caption = "Esej (ukupno u sumi za redovne studente u postocima)") %>% 
  kable_styling()
```

#### Kvartili ukupnih bodova redovnih studenata

```{r warning = FALSE}
qvrt
```

#### Kvartili ukupnih bodova redovnih studenata koji su pisali esej

```{r warning = FALSE}
qvrt_esej
```

Pojedinačni kvizovi {data-navmenu="Dodatne analize"}
=======================================================================

### Frekvencije bodova po kvizovima

```{r warning=FALSE, fig.width=10}
ggplot(kviz_kategorije, aes(x = value)) +
  geom_histogram(fill="steelblue") + 
  facet_wrap(vars(KVIZ), scales = "free", nrow = 2) +
  theme(strip.text = element_text(size = 5),
        axis.text.x = element_text(size = 5),
        axis.text.y = element_text(size = 6)) + xlab("") + ylab("")
```

Pojedinačni kvizovi po kategorijama {data-navmenu="Dodatne analize"}
=======================================================================

### Frekvencije bodova po kvizovima i kategorijama

```{r warning=FALSE, fig.width=10}
ggplot(kviz_kategorije, aes(x = value)) +
  geom_histogram(fill="steelblue") + 
  facet_grid(rows = vars(Kategorija), cols = vars(KVIZ), scales = "free") +
  theme(strip.text = element_text(size = 5),
        axis.text.x = element_text(size = 5, angle = 90),
        axis.text.y = element_text(size = 6)) + xlab("") + ylab("")
```

Pristupanje kvizovima {data-navmenu="Dodatne analize"}
=======================================================================

## Column 1 {.tabset .tabset-fade}

### Općenito

```{r warning=FALSE, fig.height=8, fig.width=8}
vis_miss(kviz %>% select(DER:K13))
```

### Varijable

```{r warning=FALSE, fig.height=8, fig.width=8}
gg_miss_var(kviz %>% select(DER:K13))
```

### Studenti

```{r warning=FALSE, fig.width=8, fig.height=8}
gg_miss_case(kviz %>% select(DER:K13))
```

### Varijable (kategorije u %)

```{r warning=FALSE, fig.width=8, fig.height=8}
gg_miss_fct(kviz %>% select(DER:Kategorija), fct = Kategorija)
```

## Column 2 {.tabset .tabset-fade}

### Općenito (klasterirano)

```{r warning=FALSE, fig.height=8, fig.width=8}
vis_miss(kviz %>% select(DER:K13), cluster = TRUE)
```

### Varijable (kategorije)

```{r warning=FALSE, fig.width=8, fig.height=8}
gg_miss_var(kviz %>% select(DER:Kategorija), facet = Kategorija)
```

### Studenti (kategorije)

```{r warning=FALSE, fig.width=8, fig.height=8}
gg_miss_case(kviz %>% select(DER:Kategorija), facet = Kategorija)
```

Pristupanje kvizovima - presječni pogled {data-navmenu="Dodatne analize"}
=======================================================================

### Pristupanje kvizovima - presječni pogled

```{r warning=FALSE, fig.height=8, fig.width=16}
gg_miss_upset(kviz %>% select(DER:K13), nset = 14, nintersect = 50)
```

Pletenica {data-navmenu="Bayes NET"}
=======================================================================

### Pletenica

```{r warning=FALSE, fig.width=12}
ggplot(bayes_klase_long2, aes(x = x, next_x = next_x, node = node, 
                              next_node = next_node, fill = factor(node),
                              label = paste0(node, ' (', pct, '%)'))) +
  geom_sankey(flow.alpha = 0.5, node.color = "black", show.legend = FALSE) +
  geom_sankey_label(size = 3, color = "black", fill= "white", hjust = -0.3) +
  theme_bw() + theme_sankey(base_size = 16) +
  theme(axis.title = element_blank(), axis.text.y = element_blank(), 
        axis.ticks = element_blank(), panel.grid = element_blank())
```

Učenje {data-navmenu="Bayes NET"}
=======================================================================

## Column 1 {.tabset .tabset-fade}

### struktura 1

```{r warning=FALSE}
graphviz.plot(learn1)
```

### struktura 2

```{r warning=FALSE}
graphviz.plot(learn2)
```

### struktura 3

```{r warning=FALSE}
graphviz.plot(learn3)
```

### struktura 4

```{r warning=FALSE}
graphviz.plot(dag3)
```

## Column 2

### Info

#### Opisi varijabli

* KOL1 - faktorska varijabla s 4 klase napravljene prema četvrtinama 
ukupnih bodova na prvom kolokviju
* KOL2 - faktorska varijabla s 4 klase napravljene prema četvrtinama 
ukupnih bodova na drugom kolokviju
* KOL3 - faktorska varijabla s 4 klase napravljene prema četvrtinama 
ukupnih bodova na trećem kolokviju
* KVIZ1 - faktorska varijabla s 4 klase napravljene prema četvrtinama 
ukupnih bodova na kvizovima prije prvog kolokvija
* KVIZ2 - faktorska varijabla s 4 klase napravljene prema četvrtinama 
ukupnih bodova na kvizovima između prvog i drugog kolokvija
* KVIZ3 - faktorska varijabla s 4 klase napravljene prema četvrtinama 
ukupnih bodova na kvizovima nakon drugog kolokvija
* ESEJ - faktorska varijabla s 4 klase napravljene prema četvrtinama 
ukupnih bodova na eseju

#### Opisi struktura

* **struktura 1** - učenje pomoću *hc* algoritma na praznom digrafu
* **struktura 2** - učenje pomoću *hc* algoritma na praznom digrafu uz zabranjene lukove
(KOL1, KVIZ1), (KOL2, KVIZ1), (KOL3, KVIZ1), (ESEJ, KVIZ1), (KOL2, KVIZ2), (KOL3, KVIZ2),
(KVIZ3, KVIZ2), (ESEJ, KVIZ2)
* **struktura 3** - učenje pomoću *gs* algoritma na praznom digrafu, neki bridovi nemaju orijentaciju
pa moramo sami odlučiti koju ćemo uzeti
* **struktura 4** - odabrane orijentacije za bridove iz **strukture 3**

### BIC score

#### struktura 1

```{r warning=FALSE}
score(learn1, data = bayes_klase, type = "bic")
```

#### struktura 2

```{r warning=FALSE}
score(learn2, data = bayes_klase, type = "bic")
```

#### struktura 4

```{r warning=FALSE}
score(dag3, data = bayes_klase, type = "bic")
```

Distribucije - struktura 1 {data-navmenu="Bayes NET"}
=======================================================================

## Column 1 {.tabset .tabset-fade}

### KOL1

```{r warning=FALSE}
bn.fit.barchart(bn1$KOL1, main = "KOL1")
```

### KOL2 | KOL1

```{r warning=FALSE}
bn.fit.barchart(bn1$KOL2, main = "KOL2 | KOL1")
```

### KOL3 | KOL2

```{r warning=FALSE}
bn.fit.barchart(bn1$KOL3, main = "KOL3 | KOL2")
```

### KVIZ1 | KOL2

```{r warning=FALSE}
bn.fit.barchart(bn1$KVIZ1, main = "KVIZ1 | KOL2")
```

### KVIZ2 | KOL2

```{r warning=FALSE}
bn.fit.barchart(bn1$KVIZ2, main = "KVIZ2 | KOL2")
```

### KVIZ3 | ESEJ

```{r warning=FALSE}
bn.fit.barchart(bn1$KVIZ3, main = "KVIZ3 | ESEJ")
```

### ESEJ | KOL2

```{r warning=FALSE}
bn.fit.barchart(bn1$ESEJ, main = "ESEJ | KOL2")
```

## Column 2 {.tabset .tabset-fade}

### struktura 1

```{r warning=FALSE}
graphviz.plot(learn1)
```

### marginalne distribucije

```{r warning=FALSE}
graphviz.chart(bn1, type = "barprob", bg = "azure", bar.col = "darkblue")
```

Distribucije - struktura 2 {data-navmenu="Bayes NET"}
=======================================================================

## Column 1 {.tabset .tabset-fade}

### KOL1 | KVIZ1

```{r warning=FALSE}
bn.fit.barchart(bn2$KOL1, main = "KOL1 | KVIZ1")
```

### KOL2 | KOL1

```{r warning=FALSE}
bn.fit.barchart(bn2$KOL2, main = "KOL2 | KOL1")
```

### KOL3 | KOL2

```{r warning=FALSE}
bn.fit.barchart(bn2$KOL3, main = "KOL3 | KOL2")
```

### KVIZ1

```{r warning=FALSE}
bn.fit.barchart(bn2$KVIZ1, main = "KVIZ1")
```

### KVIZ2 | KOL1

```{r warning=FALSE}
bn.fit.barchart(bn2$KVIZ2, main = "KVIZ2 | KOL1")
```

### KVIZ3 | ESEJ

```{r warning=FALSE}
bn.fit.barchart(bn2$KVIZ3, main = "KVIZ3 | ESEJ")
```

### ESEJ | KOL2

```{r warning=FALSE}
bn.fit.barchart(bn2$ESEJ, main = "ESEJ | KOL2")
```

## Column 2 {.tabset .tabset-fade}

### struktura 2

```{r warning=FALSE}
graphviz.plot(learn2)
```

### marginalne distribucije

```{r warning=FALSE}
graphviz.chart(bn2, type = "barprob", bg = "azure", bar.col = "darkblue")
```

Distribucije - struktura 4 {data-navmenu="Bayes NET"}
=======================================================================

## Column 1 {.tabset .tabset-fade}

### KOL1

```{r warning=FALSE}
bn.fit.barchart(bn3$KOL1, main = "KOL1")
```

### KOL2 | KOL1,KVIZ1

```{r warning=FALSE, fig.height=8, fig.width = 8}
bn.fit.barchart(bn3$KOL2, main = "KOL2 | KOL1,KVIZ1")
```

### KOL3 | KOL2,KVIZ2,KVIZ3

```{r warning=FALSE, fig.height=10}
bn.fit.barchart(bn3$KOL3, main = "KOL3 | KOL2,KVIZ2,KVIZ3")
```

### KVIZ1

```{r warning=FALSE}
bn.fit.barchart(bn3$KVIZ1, main = "KVIZ1")
```

### KVIZ2

```{r warning=FALSE}
bn.fit.barchart(bn3$KVIZ2, main = "KVIZ2")
```

### KVIZ3 | ESEJ

```{r warning=FALSE}
bn.fit.barchart(bn3$KVIZ3, main = "KVIZ3 | ESEJ")
```

### ESEJ

```{r warning=FALSE}
bn.fit.barchart(bn3$ESEJ, main = "ESEJ")
```

## Column 2 {.tabset .tabset-fade}

### struktura 4

```{r warning=FALSE}
graphviz.plot(dag3)
```

### marginalne distribucije

```{r warning=FALSE}
graphviz.chart(bn3, type = "barprob", bg = "azure", bar.col = "darkblue")
```

Postavljanje upita - struktura 2 {data-navmenu="Bayes NET"}
=======================================================================

## Column 1

### P(ESEJ | KOL1, KOL2)

```{r warning=FALSE}
junc2 <- compile(as.grain(bn2))
querygrain(junc2, nodes = c("ESEJ", "KOL1", "KOL2"), type = "conditional")
```

## Column 2

### P(ESEJ, KOL1, KOL2)

```{r warning=FALSE}
querygrain(junc2, nodes = c("ESEJ", "KOL1", "KOL2"), type = "joint")
```

Predikcije eseja - struktura 1 {data-navmenu="Bayes NET"}
=======================================================================

## Column 1

###  Metrike na testnom i trening skupu {data-height=200}

```{r}
tab1 <- rf_metrike(mat1, truth = truth, estimate = pred, `1`:`4`)
tab2 <- rf_metrike(mat1_tr, truth = truth, estimate = pred, `1`:`4`)
tab1 %>% inner_join(tab2, by = ".metric") %>%
  select(.metric, .estimator = .estimator.x, trening = .estimate.y, test = .estimate.x)
```

### Confusion matrix

```{r}
conf_mat(mat1, truth = truth, estimate = pred) %>% autoplot("heatmap") +
  scale_fill_gradient(low = "#87DEE7",
                       high = "#FFFFCC")
```

## Column 2

### ROC curve

```{r}
mat1 %>% roc_curve(truth, `1`:`4`) %>% autoplot()
```

### Gain curve

```{r}
mat1 %>% gain_curve(truth, `1`:`4`) %>% autoplot()
```

Predikcije eseja - struktura 2 {data-navmenu="Bayes NET"}
=======================================================================

## Column 1

###  Metrike na testnom i trening skupu {data-height=200}

```{r}
tab1 <- rf_metrike(mat2, truth = truth, estimate = pred, `1`:`4`)
tab2 <- rf_metrike(mat2_tr, truth = truth, estimate = pred, `1`:`4`)
tab1 %>% inner_join(tab2, by = ".metric") %>%
  select(.metric, .estimator = .estimator.x, trening = .estimate.y, test = .estimate.x)
```

### Confusion matrix

```{r}
conf_mat(mat2, truth = truth, estimate = pred) %>% autoplot("heatmap") +
  scale_fill_gradient(low = "#87DEE7",
                       high = "#FFFFCC")
```

## Column 2

### ROC curve

```{r}
mat2 %>% roc_curve(truth, `1`:`4`) %>% autoplot()
```

### Gain curve

```{r}
mat2 %>% gain_curve(truth, `1`:`4`) %>% autoplot()
```

Predikcije eseja - struktura 4 {data-navmenu="Bayes NET"}
=======================================================================

## Column 1

###  Metrike na testnom i trening skupu {data-height=200}

```{r}
tab1 <- rf_metrike(mat3, truth = truth, estimate = pred, `1`:`4`)
tab2 <- rf_metrike(mat3_tr, truth = truth, estimate = pred, `1`:`4`)
tab1 %>% inner_join(tab2, by = ".metric") %>%
  select(.metric, .estimator = .estimator.x, trening = .estimate.y, test = .estimate.x)
```

### Confusion matrix

```{r}
conf_mat(mat3, truth = truth, estimate = pred) %>% autoplot("heatmap") +
  scale_fill_gradient(low = "#87DEE7",
                       high = "#FFFFCC")
```

## Column 2

### ROC curve

```{r}
mat3 %>% roc_curve(truth, `1`:`4`) %>% autoplot()
```

### Gain curve

```{r}
mat3 %>% gain_curve(truth, `1`:`4`) %>% autoplot()
```

Opis modela {data-navmenu="Forest"}
=======================================================================

### Opis modela

**Prediktori**

- KVIZ - suma bodova na kvizovima
- DZ - suma bodova na domaćim zadaćama
- KOL1 - broj bodova na prvom kolokviju 
- KOL2 - broj bodova na drugom kolokviju 
- KOL3 - broj bodova na trećem kolokviju 

**Response** - Klasifikacija studenata na temelju broja bodova eseju.

- **1** - ako je na eseju ukupni broj bodova unutar intervala [0, 2.5]
- **2** - ako je na eseju ukupni broj bodova unutar intervala (2.5, 5]
- **3** - ako je na eseju ukupni broj bodova unutar intervala (5, 7.5]
- **4** - ako je na eseju ukupni broj bodova unutar intervala (7.5, 10]

**Hiperparametri** - za odabir optimalne kombinacije hiperparametara napravljen je 
cross-validation s 10 *kutija* pri čemu je na slučajni način isprobano 1000 kombinacija 
hiperparametara *mtry*, *min_n* i *trees*. Najbolji model je odabran s obzirom na *roc_auc*
metriku.

- *mtry* - uzimane su vrijednosti iz skupa {2, 3, 4}
- *trees* - uzimani su prirodni brojevi između 400 i 1500
- *min_n* - uzimani su prirodni brojevi između 2 i 40.

Hiperparametri {data-navmenu="Forest"}
=======================================================================

## Column 1

### Testirani hiperparametri

```{r}
RF_tuning %>%
  collect_metrics() %>%
  filter(.metric == "roc_auc", trees > 0) %>%
  pivot_longer(cols = mtry:min_n) %>%
  mutate(best_mod = mean == max(mean)) %>% 
  ggplot(aes(x = value, y = mean)) +
  #geom_line(alpha = 0.5, size = 1.5) +
  geom_point(aes(color = best_mod), size = 0.3,) +
  facet_wrap(~name, scales = "free_x") +
  scale_x_continuous() +
  labs(y = "roc auc", x = "", color = "Best Model")
```

## Column 2

### 200 najboljih modela za roc_auc metriku

```{r}
print(RF_tuning %>% show_best(metric = 'roc_auc', n = 200), n = 200)
```

### 200 najboljih modela za mcc metriku

```{r}
print(RF_tuning %>% show_best(metric = 'mcc', n = 200), n = 200)
```

Efikasnost {data-navmenu="Forest"}
=======================================================================

## Column 1

###  Metrike na testnom i trening skupu {data-height=200}

```{r}
tab1 <- RF_fit %>% collect_predictions() %>% 
  rf_metrike(truth = ESEJ, estimate = .pred_class, .pred_1:.pred_4)
tab2 <- RF_work %>%
  rf_metrike(truth = ESEJ, estimate = .pred_class, .pred_1:.pred_4)
tab1 %>% inner_join(tab2, by = ".metric") %>%
  select(.metric, .estimator = .estimator.x, trening = .estimate.y, test = .estimate.x)
```

### Confusion matrix

```{r}
RF_fit %>% collect_predictions() %>%
  conf_mat(truth = ESEJ, estimate = .pred_class) %>% autoplot("heatmap") +
  scale_fill_gradient(low = "#87DEE7",
                       high = "#FFFFCC")
```

## Column 2

### ROC curve

```{r}
RF_fit %>% collect_predictions() %>% roc_curve(ESEJ, .pred_1:.pred_4) %>% autoplot()
```

### Gain curve

```{r}
RF_fit %>% collect_predictions() %>% gain_curve(ESEJ, .pred_1:.pred_4) %>% autoplot()
```

Važnost prediktora {data-navmenu="Forest"}
=======================================================================

## Columnn 1

### Gini

```{r}
RF_fit %>% extract_fit_parsnip() %>% vip()
```

### permutacija

```{r}
RF_fit_perm %>% extract_fit_parsnip() %>% vip()
```

## Column 2

### Boruta

```{r}
plot(RF_Boruta, cex.axis=.7, las=2, xlab="",
     colCode = c("green", "orange", "#f6546a", "#2acaea"))
```

### Boruta (history)

```{r}
plotImpHistory(RF_Boruta, colCode = c("green", "orange", "#f6546a", "#2acaea"))
```