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data.org Financial Health Prediction Challenge

Helping Eswatini, Lesotho
and 2 other countries
  • Eswatini
  • Lesotho
  • Zimbabwe
  • Malawi
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$1 500 USD
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Prediction
Machine Learning
1580 joined
857 active
Starti
Dec 12, 25
Closei
Mar 15, 26
Reveali
Mar 16, 26
User avatar
Moujoudix
Which F1-score ?
Data · 29 Dec 2025, 18:45 · 5

Hi everyone, I hope you are doing well. the challenge's description says : The evaluation metric for this challenge is the F1 score. Any clue which one is it ? macro-F1 or weighted-F1 ? Thanks.

Discussion 5 answers
User avatar
CodeJoe

Weighted-F1

6 Jan 2026, 22:20
Upvotes 2
User avatar
Moujoudix

Thanks @CodeJoe, can you send the source please ? i can't find it in the challenge description.

User avatar
CodeJoe

I deciphered it from the scores, Macro scores were way less (~80) whereby weighted looks very similar to the LB.

User avatar
Moujoudix

I see, i'll investigate it later. Thanks for your help 🙏

User avatar
Moujoudix

after further investigation, @CodeJoe was actually right, the challeng is using weighted-F1.

i've tested it using this code snippet :

import math
def p_from_score_macro(S):
    # S = (1/3) * 2p/(1+p)  ->  p = 3S/(2-3S)
    return (3*S) / (2 - 3*S)
def p_from_score_weighted(S):
    # S = 2p^2/(1+p) -> 2p^2 - S p - S = 0
    return (S + math.sqrt(S*S + 8*S)) / 4
  
def p_from_score_micro(S):
    # micro-F1 == accuracy
    return S

# S_low, S_med, S_high from the LB:
S_low, S_med, S_high = 0.0, 0.0, 0.0  # fill these
for name, f in [
    ("macro", p_from_score_macro),
    ("weighted", p_from_score_weighted),
    ("micro", p_from_score_micro),]:
    ps = [f(S_low), f(S_med), f(S_high)]
    print(name, ps, "sum =", sum(ps))
30 Jan 2026, 00:05
Upvotes 2