machine-learning
Machine learning development patterns, model training, evaluation, and deployment. Use when building ML pipelines, training models, feature engineering, model evaluation, or deploying ML systems to production.
Best use case
machine-learning is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. Machine learning development patterns, model training, evaluation, and deployment. Use when building ML pipelines, training models, feature engineering, model evaluation, or deploying ML systems to production.
Machine learning development patterns, model training, evaluation, and deployment. Use when building ML pipelines, training models, feature engineering, model evaluation, or deploying ML systems to production.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "machine-learning" skill to help with this workflow task. Context: Machine learning development patterns, model training, evaluation, and deployment. Use when building ML pipelines, training models, feature engineering, model evaluation, or deploying ML systems to production.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/machine-learning/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How machine-learning Compares
| Feature / Agent | machine-learning | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Machine learning development patterns, model training, evaluation, and deployment. Use when building ML pipelines, training models, feature engineering, model evaluation, or deploying ML systems to production.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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. Model Selection **Algorithm Selection Guide:** | Data Size | Problem | Recommended Models | |-----------|---------|-------------------| | Small (<10K) | Classification | Logistic Regression, SVM, Random Forest | | Small (<10K) | Regression | Linear Regression, Ridge, SVR | | Medium (10K-1M) | Classification | XGBoost, LightGBM, Neural Networks | | Medium (10K-1M) | Regression | XGBoost, LightGBM, Neural Networks | | Large (>1M) | Any | Deep Learning, Distributed training | | Tabular | Any | Gradient Boosting (XGBoost, LightGBM, CatBoost) | | Images | Classification | CNN, ResNet, EfficientNet, Vision Transformers | | Text | NLP | Transformers (BERT, RoBERTa, GPT) | | Sequential | Time Series | LSTM, Transformer, Prophet | ### 4. Model Training **Hyperparameter Tuning:** - Grid Search: exhaustive, good for small spaces - Random Search: efficient, good for large spaces - Bayesian Optimization: smart exploration (Optuna, Hyperopt) - Early stopping: prevent overfitting **Common Hyperparameters:** | Model | Key Parameters | |-------|---------------| | XGBoost | learning_rate, max_depth, n_estimators, subsample | | LightGBM | num_leaves, learning_rate, n_estimators, feature_fraction | | Random Forest | n_estimators, max_depth, min_samples_split | | Neural Networks | learning_rate, batch_size, layers, dropout | ### 5. Model Evaluation **Evaluation Best Practices:** - Always use held-out test set for final evaluation - Use cross-validation during development - Check for overfitting (train vs validation gap) - Evaluate on multiple metrics - Analyze errors qualitatively **Handling Imbalanced Data:** - Resampling: SMOTE, undersampling - Class weights: weighted loss functions - Threshold tuning: optimize decision threshold - Evaluation: use PR-AUC over ROC-AUC ### 6. Production Deployment **Model Serving Patterns:** - REST API (Flask, FastAPI, TF Serving) - Batch inference (scheduled jobs) - Streaming (real-time predictions) - Edge deployment (mobile, IoT) **Production Considerations:** - Latency requirements (p50, p95, p99) - Throughput (requests per second) - Model size and memory footprint - Fallback strategies - A/B testing framework ### 7. Monitoring & Maintenance **What to Monitor:** - Prediction latency - Input feature distributions (data drift) - Prediction distributions (concept drift) - Model performance metrics - Error rates and types **Retraining Triggers:** - Performance degradation below threshold - Significant data drift detected - Scheduled retraining (daily, weekly) - New training data available ## MLOps Best Practices ### Experiment Tracking Track for every experiment: - Code version (git commit) - Data version (hash or version ID) - Hyperparameters - Metrics (train, validation, test) - Model artifacts - Environment (packages, versions) ### Model Versioning ``` models/ ├── model_v1.0.0/ │ ├── model.pkl │ ├── metadata.json │ ├── requirements.txt │ └── metrics.json ├── model_v1.1.0/ └── model_v2.0.0/ ``` ### CI/CD for ML 1. **Continuous Integration:** - Data validation tests - Model training tests - Performance regression tests 2. **Continuous Deployment:** - Staging environment validation - Shadow mode testing - Gradual rollout (canary) - Automatic rollback ## Reference Files For detailed patterns and code examples, load reference files as needed: - **`references/preprocessing.md`** - Data preprocessing patterns and feature engineering techniques - **`references/model_patterns.md`** - Model architecture patterns and implementation examples - **`references/evaluation.md`** - Comprehensive evaluation strategies and metrics ## Integration with Other Skills - **performance** - For optimizing inference latency - **testing** - For ML-specific testing patterns - **database-optimization** - For feature store queries - **debugging** - For model debugging and error analysis
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