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Machine learning development patterns, model training, evaluation, and deployment.
Passed 22 of 22 test cases with the skill, and 20 of 22 without it.
2 fixed20 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
Which reference file details comprehensive evaluation strategies and metrics?
| Checked | Without skill | With skill |
|---|---|---|
| References references/evaluation.md | Missed | Passed |
The instructions your model reads when the skill loads.
Comprehensive machine learning skill covering the full ML lifecycle from experimentation to production deployment.
Classification Types:
Success Metrics by Problem Type:
| Problem Type | Primary Metrics | Secondary Metrics |
|---|---|---|
| Binary Classification | AUC-ROC, F1 | Precision, Recall, PR-AUC |
| Multi-class | Macro F1, Accuracy | Per-class metrics |
| Regression | RMSE, MAE | R², MAPE |
| Ranking | NDCG, MAP | MRR |
| Clustering | Silhouette, Calinski-Harabasz | Davies-Bouldin |
test cases passed, vs 20 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.