Agent skill · research science · aiskillstore
machine-learning
Machine learning development patterns, model training, evaluation, and
Why this skill is useful
Adds comprehensive patterns and scripts for ML pipeline development, model training, and evaluation that are not commonly found in public resources.
What it needs
About 3k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Machine Learning Comprehensive machine learning skill covering the full ML lifecycle from experimentation to production deployment. When to Use This Skill Building machine learning pipelines Feature engineering and data preprocessing Model training, evaluation, and selection Hyperparameter tuning and optimization Model deployment and serving ML experiment tracking and versioning Production ML monitoring and maintenance ML Development Lifecycle 1. Problem Definition Classification Types: Binary classification (spam/not spam) Multi-class classification (image categories) Multi-label classification (document tags) Regression (price prediction) Clustering (customer segmentation) Ranking (search results) Anomaly detection (fraud detection) 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 2. Data Preparation Data Quality Checks: Missing value analysis and imputation strategies Outlier detection and handling Data type validation Distribution analysis Target leakage detection Feature Engineering Patterns: Numerical: scaling, binning, log transforms, polynomial features Categorical: one-hot, target encoding, frequency encoding, embeddings Temporal: lag features, rolling statistics, cyclical encoding Text: TF-IDF, word embeddings, transformer embeddings Geospatial: distance features, clustering, grid encoding Train/Test Split Strategies: Random split (standard) Stratified split (imbalanced classes) Time-based split (temporal data) Group split (prevent data leakage) K-fold cross-validation 3. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/machine-learning