Well done everyone! Let's have a look at what you have accomplished in this round. Below you can see an image that represents the overall scores achieved for classification and regression tasks. Lower scores are better for regression and higher is better for classification , so try to improve your performance at each round.

Let's also compare with the previous rounds and see how many of you improved their rank compared to previous round.

I know everyone is curious about the leader board. Since revealing the student ID's is not right and sharing your deanonymized code will give access to your personalized pages, I am just sharing the top 50 scores scores below. Students with the same scores are more likely to be a team mates and I will share more information on that below.

CLASSIFICATION LEADERBOARD - ACCURACY

classification classification_rank
1 0.931718 1.0
2 0.931718 1.0
3 0.931718 1.0
4 0.931718 1.0
5 0.931718 1.0
6 0.892070 6.0
7 0.889868 7.0
8 0.887665 8.0
9 0.887665 8.0
10 0.887665 8.0
11 0.887665 8.0
12 0.887665 8.0
13 0.881057 13.0
14 0.881057 13.0
15 0.881057 13.0
16 0.881057 13.0
17 0.874449 17.0
18 0.874449 17.0
19 0.874449 17.0
20 0.874449 17.0
21 0.874449 17.0
22 0.872247 22.0
23 0.872247 22.0
24 0.872247 22.0
25 0.867841 25.0
26 0.867841 25.0
27 0.867841 25.0
28 0.867841 25.0
29 0.856828 29.0
30 0.856828 29.0
31 0.856828 29.0
32 0.852423 32.0
33 0.848018 33.0
34 0.848018 33.0
35 0.848018 33.0
36 0.848018 33.0
37 0.848018 33.0
38 0.848018 33.0
39 0.843612 39.0
40 0.843612 39.0
41 0.843612 39.0
42 0.843612 39.0
43 0.841410 43.0
44 0.841410 43.0
45 0.841410 43.0
46 0.832599 46.0
47 0.832599 46.0
48 0.832599 46.0
49 0.832599 46.0
50 0.828194 50.0

CLASSIFICATION LEADERBOARD - F1Weighted

classificationV2 classificationV2_rank
1 0.930784 1.0
2 0.930784 1.0
3 0.930784 1.0
4 0.930784 1.0
5 0.930784 1.0
6 0.889420 6.0
7 0.887522 7.0
8 0.886219 8.0
9 0.886219 8.0
10 0.886219 8.0
11 0.886219 8.0
12 0.886219 8.0
13 0.879205 13.0
14 0.879205 13.0
15 0.879205 13.0
16 0.879205 13.0
17 0.872131 17.0
18 0.872131 17.0
19 0.872131 17.0
20 0.872131 17.0
21 0.872131 17.0
22 0.868805 22.0
23 0.868805 22.0
24 0.868805 22.0
25 0.867003 25.0
26 0.867003 25.0
27 0.867003 25.0
28 0.867003 25.0
29 0.854857 29.0
30 0.854857 29.0
31 0.854857 29.0
32 0.850515 32.0
33 0.842258 33.0
34 0.842258 33.0
35 0.842258 33.0
36 0.842258 33.0
37 0.842067 37.0
38 0.842067 37.0
39 0.842067 37.0
40 0.840368 40.0
41 0.840368 40.0
42 0.840368 40.0
43 0.839486 43.0
44 0.838710 44.0
45 0.838710 44.0
46 0.830608 46.0
47 0.830608 46.0
48 0.830608 46.0
49 0.830608 46.0
50 0.823784 50.0

REGRESSION LEADERBOARD

regression regression_rank
1 553.378650 1.0
2 553.378650 1.0
3 553.378650 1.0
4 577.577124 4.0
5 577.577124 4.0
6 577.577124 4.0
7 597.061185 7.0
8 597.061185 7.0
9 597.061185 7.0
10 597.061185 7.0
11 637.920775 11.0
12 644.951105 12.0
13 644.951105 12.0
14 669.069913 14.0
15 676.935979 15.0
16 676.935979 15.0
17 676.935979 15.0
18 676.935979 15.0
19 676.935979 15.0
20 677.949998 20.0
21 679.645403 21.0
22 679.645403 21.0
23 689.308486 23.0
24 717.762314 24.0
25 717.762314 24.0
26 717.762314 24.0
27 727.472737 27.0
28 727.472737 27.0
29 727.472737 27.0
30 727.472737 27.0
31 727.472737 27.0
32 727.472737 27.0
33 727.472737 27.0
34 727.472737 27.0
35 727.472737 27.0
36 727.472737 27.0
37 727.472737 27.0
38 727.472737 27.0
39 727.472737 27.0
40 727.472737 27.0
41 761.794131 41.0
42 761.794131 41.0
43 909.079815 43.0
44 909.079815 43.0
45 909.079815 43.0
46 909.079815 43.0
47 909.079815 43.0
48 943.986413 48.0
49 1021.809554 49.0
50 1021.809554 49.0