scikit-learn - Build and evaluate scikit-learn ML workflows
Use scikit-learn for supervised and unsupervised learning, preprocessing, evaluation, hyperparameter tuning, and reproducible ML pipelines.
Tags
Updated: 2026-10-01Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Build classification models
- Build regression models
- Perform clustering
- Reduce feature dimensions
- Preprocess machine learning data
- Evaluate model performance
- Tune model hyperparameters
- Build reproducible pipelines
- Compare machine learning algorithms
Inputs
- Training data
- Test data
- Target labels
- Feature definitions
- Machine learning task requirements
- Model configuration
- Hyperparameter search space
Outputs
- Trained machine learning models
- Predictions
- Evaluation metrics
- Cross-validation results
- Tuned hyperparameters
- Preprocessing pipelines
- Clustering results
- Dimensionality-reduced data
- Model comparison results
- Data visualizations
Requirements
- Python environment
- scikit-learn package
- Optional matplotlib and seaborn packages
- Optional pandas and numpy packages