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 0.935982 1.0
2 0.935982 1.0
3 0.935982 1.0
4 0.935982 1.0
5 0.935982 1.0
6 0.935982 1.0
7 0.935982 1.0
8 0.933775 8.0
9 0.918322 9.0
10 0.909492 10.0
11 0.909492 10.0
12 0.909492 10.0
13 0.909492 10.0
14 0.909492 10.0
15 0.907285 15.0
16 0.907285 15.0
17 0.907285 15.0
18 0.907285 15.0
19 0.907285 15.0
20 0.905077 20.0
21 0.905077 20.0
22 0.905077 20.0
23 0.905077 20.0
24 0.905077 20.0
25 0.902870 25.0
26 0.902870 25.0
27 0.902870 25.0
28 0.902870 25.0
29 0.898455 29.0
30 0.898455 29.0
31 0.887417 31.0
32 0.887417 31.0
33 0.887417 31.0
34 0.887417 31.0
35 0.887417 31.0
36 0.885210 36.0
37 0.885210 36.0
38 0.885210 36.0
39 0.880795 39.0
40 0.880795 39.0
41 0.880795 39.0
42 0.880795 39.0
43 0.880795 39.0
44 0.880795 39.0
45 0.880795 39.0
46 0.880795 39.0
47 0.880795 39.0
48 0.878587 48.0
49 0.878587 48.0
50 0.878587 48.0

CLASSIFICATION LEADERBOARD - F1Weighted

classificationV2 classificationV2_rank
1 0.935799 1.0
2 0.935799 1.0
3 0.935799 1.0
4 0.935799 1.0
5 0.934822 5.0
6 0.934822 5.0
7 0.934822 5.0
8 0.933353 8.0
9 0.916709 9.0
10 0.907014 10.0
11 0.907014 10.0
12 0.907014 10.0
13 0.907014 10.0
14 0.907014 10.0
15 0.905207 15.0
16 0.905207 15.0
17 0.905207 15.0
18 0.905207 15.0
19 0.905207 15.0
20 0.904697 20.0
21 0.904697 20.0
22 0.904697 20.0
23 0.904697 20.0
24 0.904697 20.0
25 0.900246 25.0
26 0.900246 25.0
27 0.900246 25.0
28 0.900246 25.0
29 0.895816 29.0
30 0.895816 29.0
31 0.883928 31.0
32 0.883113 32.0
33 0.883113 32.0
34 0.883113 32.0
35 0.882594 35.0
36 0.882594 35.0
37 0.882594 35.0
38 0.882594 35.0
39 0.879595 39.0
40 0.879595 39.0
41 0.879595 39.0
42 0.879595 39.0
43 0.879595 39.0
44 0.879272 44.0
45 0.879272 44.0
46 0.879272 44.0
47 0.879272 44.0
48 0.878196 48.0
49 0.878196 48.0
50 0.878196 48.0

REGRESSION LEADERBOARD

regression regression_rank
1 542.782531 1.0
2 542.782531 1.0
3 542.782531 1.0
4 555.132957 4.0
5 555.132957 4.0
6 555.132957 4.0
7 555.132957 4.0
8 583.297143 8.0
9 605.252479 9.0
10 605.252479 9.0
11 605.252479 9.0
12 605.252479 9.0
13 607.969363 13.0
14 607.969363 13.0
15 607.969363 13.0
16 629.796240 16.0
17 629.796240 16.0
18 629.796240 16.0
19 631.273073 19.0
20 640.165077 20.0
21 640.165077 20.0
22 642.696489 22.0
23 642.696489 22.0
24 642.696489 22.0
25 642.696489 22.0
26 649.229094 26.0
27 649.229094 26.0
28 649.229094 26.0
29 649.229094 26.0
30 649.229094 26.0
31 678.437312 31.0
32 699.989196 32.0
33 728.184747 33.0
34 728.184747 33.0
35 728.184747 33.0
36 728.184747 33.0
37 728.184747 33.0
38 729.806419 38.0
39 729.806419 38.0
40 740.543585 40.0
41 740.543585 40.0
42 740.543585 40.0
43 740.543585 40.0
44 740.543585 40.0
45 740.543585 40.0
46 742.813765 46.0
47 754.877463 47.0
48 754.877463 47.0
49 754.877463 47.0
50 754.877463 47.0