Agent skill · research science · sickn33
scikit-learn
Machine learning in Python with scikit-learn. Use for classification, regression, clustering, model evaluation, and ML pipelines.
Why this skill is useful
Provides specific implementation patterns and model evaluation techniques using scikit-learn that the AI wouldn't reliably generate on its own.
What it needs
Requires matplotlib, numpy, pandas, scikit-learn, seaborn installed locally. About 1k tokens when loaded. Last updated 2026-08-07. 44,571 stars on the source repository.
What this skill does
Scikit-learn Detailed Guide Read the detailed guide before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely. When to Use This Skill Use the scikit-learn skill when: Building classification or regression models Performing clustering or dimensionality reduction Preprocessing and transforming data for machine learning Evaluating model performance with cross-validation Tuning hyperparameters with grid or random search Creating ML pipelines for production workflows Comparing different algorithms for a task Working with both structured (tabular) and text data Need interpretable, classical machine learning approaches Example Scripts Classification Pipeline Run a complete classification workflow with preprocessing, model comparison, hyperparameter tuning, and evaluation: This script demonstrates: Handling mixed data types (numeric and categorical) Model comparison using cross-validation Hyperparameter tuning with GridSearchCV Comprehensive evaluation with multiple metrics Feature importance analysis Clustering Analysis Perform clustering analysis with algorithm comparison and visualization: This script demonstrates: Finding optimal number of clusters (elbow method, silhouette analysis) Comparing multiple clustering algorithms (K-Means, DBSCAN, Agglomerative, Gaussian Mixture) Evaluating clustering quality without ground truth Visualizing results with PCA projection Limitations Use this skill only when the task clearly matches the scope described above. Do not treat the output as a substitute for environment-specific validation, testing, or expert review. Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills sickn33/scikit-learn--5eda16