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.

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 0.902655 1.0
2 0.902655 1.0
3 0.902655 1.0
4 0.902655 1.0
5 0.902655 1.0
6 0.882743 6.0
7 0.882743 6.0
8 0.882743 6.0
9 0.882743 6.0
10 0.882743 6.0
11 0.871681 11.0
12 0.871681 11.0
13 0.871681 11.0
14 0.871681 11.0
15 0.860619 15.0
16 0.853982 16.0
17 0.853982 16.0
18 0.853982 16.0
19 0.853982 16.0
20 0.829646 20.0
21 0.820796 21.0
22 0.820796 21.0
23 0.820796 21.0
24 0.803097 24.0
25 0.803097 24.0
26 0.803097 24.0
27 0.803097 24.0
28 0.803097 24.0
29 0.796460 29.0
30 0.794248 30.0
31 0.794248 30.0
32 0.794248 30.0
33 0.792035 33.0
34 0.792035 33.0
35 0.792035 33.0
36 0.787611 36.0
37 0.783186 37.0
38 0.783186 37.0
39 0.780973 39.0
40 0.780973 39.0
41 0.780973 39.0
42 0.780973 39.0
43 0.778761 43.0
44 0.774336 44.0
45 0.772124 45.0
46 0.763274 46.0
47 0.763274 46.0
48 0.747788 48.0
49 0.747788 48.0
50 0.747788 48.0

CLASSIFICATION LEADERBOARD - F1Weighted

classificationV2 classificationV2_rank
1 0.899095 1.0
2 0.899095 1.0
3 0.899095 1.0
4 0.899095 1.0
5 0.899095 1.0
6 0.880853 6.0
7 0.880853 6.0
8 0.880853 6.0
9 0.880853 6.0
10 0.880853 6.0
11 0.868743 11.0
12 0.868743 11.0
13 0.868743 11.0
14 0.868743 11.0
15 0.856303 15.0
16 0.850711 16.0
17 0.850711 16.0
18 0.850711 16.0
19 0.850711 16.0
20 0.823629 20.0
21 0.819678 21.0
22 0.819678 21.0
23 0.816591 23.0
24 0.803258 24.0
25 0.803258 24.0
26 0.803258 24.0
27 0.803258 24.0
28 0.803258 24.0
29 0.792156 29.0
30 0.788863 30.0
31 0.788863 30.0
32 0.788863 30.0
33 0.786513 33.0
34 0.785595 34.0
35 0.785595 34.0
36 0.785595 34.0
37 0.785517 37.0
38 0.785517 37.0
39 0.780469 39.0
40 0.773254 40.0
41 0.773254 40.0
42 0.772790 42.0
43 0.772003 43.0
44 0.771338 44.0
45 0.770159 45.0
46 0.765389 46.0
47 0.765389 46.0
48 0.742388 48.0
49 0.742388 48.0
50 0.742388 48.0

REGRESSION LEADERBOARD

regression regression_rank
1 564.116308 1.0
2 564.116308 1.0
3 564.116308 1.0
4 656.052274 4.0
5 660.553606 5.0
6 660.553606 5.0
7 677.317796 7.0
8 711.241557 8.0
9 711.241557 8.0
10 711.241557 8.0
11 716.761511 11.0
12 716.761511 11.0
13 727.461655 13.0
14 727.461655 13.0
15 727.461655 13.0
16 727.461655 13.0
17 727.461655 13.0
18 727.461655 13.0
19 727.461655 13.0
20 727.461655 13.0
21 727.461655 13.0
22 727.461655 13.0
23 727.461655 13.0
24 727.461655 13.0
25 727.461655 13.0
26 727.461655 13.0
27 729.131388 27.0
28 752.974216 28.0
29 752.974216 28.0
30 752.974216 28.0
31 752.974216 28.0
32 752.974216 28.0
33 885.712889 33.0
34 885.712889 33.0
35 1047.438738 35.0
36 1047.438738 35.0
37 1047.438738 35.0
38 1238.984546 38.0
39 1238.984546 38.0
40 1238.984546 38.0
41 1238.984546 38.0
42 1467.109000 42.0
43 1524.256362 43.0
44 1586.113273 44.0
45 1586.113273 44.0
46 1586.113273 44.0
47 1586.113273 44.0
48 1586.113273 44.0
49 1634.455306 49.0
50 2069.828315 50.0