DigiCow Farmer Training Adoption Challenge
'/%3e%3c/defs%3e%3cpath fill='%23fff' d='M-120-80h240V80h-240z'/%3e%3cpath d='M-120-80h240v48h-240z'/%3e%3cpath fill='%23060' d='M-120 32h240v48h-240z'/%3e%3cg id='b'%3e%3cuse xlink:href='%23a' stroke='%23000'/%3e%3cuse xlink:href='%23a' fill='%23fff'/%3e%3c/g%3e%3cuse xlink:href='%23b' transform='scale(-1 1)'/%3e%3cpath fill='%23b00' d='M-120-24v48h101c3 8 13 24 19 24s16-16 19-24h101v-48H19C16-32 6-48 0-48s-16 16-19 24z'/%3e%3cpath id='c' d='M19 24c3-8 5-16 5-24s-2-16-5-24c-3 8-5 16-5 24s2 16 5 24'/%3e%3cuse xlink:href='%23c' transform='scale(-1 1)'/%3e%3cg fill='%23fff'%3e%3cellipse rx='4' ry='6'/%3e%3cpath id='d' d='M1 5.85s4 8 4 21-4 21-4 21z'/%3e%3cuse xlink:href='%23d' transform='scale(-1)'/%3e%3cuse xlink:href='%23d' transform='scale(-1 1)'/%3e%3cuse xlink:href='%23d' transform='scale(1 -1)'/%3e%3c/g%3e%3c/svg%3e)
Helping Kenya
€8 250 EUR
Under code review
Data analysis
Classification
895 joined
388 active
Start
Jan 28, 26
Close
Mar 01, 26
Reveal
Mar 02, 26
'%3e%3cpath fill='%23001489' d='M0 0v6h9V0z'/%3e%3cpath fill='%23e03c31' d='M0 0v3h9V0z'/%3e%3cg stroke='%23fff' stroke-width='2'%3e%3cpath id='d' d='m0 0 4.5 3L0 6m4.5-3H9'/%3e%3cuse xlink:href='%23b' stroke='%23ffb81c' clip-path='url(%23c)'/%3e%3c/g%3e%3cuse xlink:href='%23d' fill='none' stroke='%23007749' stroke-width='1.2'/%3e%3c/g%3e%3c/svg%3e){kind=small}
Multi-Metric Weighting
17 Feb 2026, 08:57 · 4
Hi all,
I’d like to clarify the score weighting, as this has come up in several chats.
The evaluation metric is a weighted multi-metric composed of Log Loss (75%) and AUC (25%). However, this 75%/25% split is not distributed evenly across the three prediction targets.
From a practical perspective, the 7-day target is more operationally useful than the 90- or 120-day targets. As a result, the 7-day target carries a higher weight in the overall score.
Happy coding!
Thanks for sharing this, noticed this too.
could you share the function then so that we can evaluate our models locally with that?
{Apparently, these are the weights that are being used :(
Here is a function to calculate the LB score:def calculate_weighted_score(target_07_auc,target_07_logloss,target_90_auc,target_90_logloss,target_120_auc,target_120_logloss):SCALING = 0.56926target_07_logloss_norm = 1 - (target_07_logloss / SCALING)target_90_logloss_norm = 1 - (target_90_logloss / SCALING)target_120_logloss_norm = 1 - (target_120_logloss / SCALING)weighted_score = (target_07_auc * 0.15 +target_07_logloss_norm * 0.65 +target_90_auc * 0.05 +target_90_logloss_norm * 0.05 +target_120_auc * 0.05 +target_120_logloss_norm * 0.05)return weighted_score