Problem library
ML building blocks, one implementation at a time.
Practice the functions, model classes, and training loops inside production ML systems.
Precision@K
Measure relevant results among the highest-scored k examples.
Recall@K
Measure how many relevant items appear in the top k results.
NDCG
Normalize discounted gain against the best possible ranking.
Mean Average Precision
Average Precision over every relevant item in the ranked list.
AUC
Measure pairwise ordering quality across positive and negative examples.
Log Loss
Evaluate probabilistic predictions with numerical stability.
Calibration Error
Measure how far predicted probabilities are from empirical positive rates.
Weighted AUC
Implement Weighted AUC with production-minded edge case handling.
Grouped AUC
Implement Grouped AUC with production-minded edge case handling.