name	scikit-learn
description	Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
license	BSD-3-Clause license
allowed-tools	Read Write Edit Bash
compatibility	Requires Python 3.11+ and scikit-learn 1.7+. NumPy and SciPy are required dependencies. Optional matplotlib/seaborn for bundled example scripts that save plots.

Scikit-learn

Overview

This skill provides comprehensive guidance for machine learning tasks using scikit-learn, the industry-standard Python library for classical machine learning. Use this skill for classification, regression, clustering, dimensionality reduction, preprocessing, model evaluation, and building production-ready ML pipelines.

Installation

Tested against scikit-learn 1.8.0 (stable; December 2025). Requires Python 3.11–3.14 (free-threaded CPython 3.14 wheels available in 1.8+).

Install the PyPI package scikit-learn (not the deprecated sklearn package on PyPI). Import in code as sklearn.

# Install scikit-learn using uv
uv pip install "scikit-learn>=1.7"

# Optional: plotting utilities and bundled script dependencies
uv pip install "scikit-learn[plots]" matplotlib seaborn

# Commonly used with
uv pip install pandas numpy

Check your version:

import sklearn
print(sklearn.__version__)

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

Quick Start

Classification Example

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report

# Split data
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, stratify=y, random_state=42
)

# Preprocess
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Train model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train_scaled, y_train)

# Evaluate
y_pred = model.predict(X_test_scaled)
print(classification_report(y_test, y_pred))

Complete Pipeline with Mixed Data

from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.ensemble import GradientBoostingClassifier

# Define feature types
numeric_features = ['age', 'income']
categorical_features = ['gender', 'occupation']

# Create preprocessing pipelines
numeric_transformer = Pipeline([
    ('imputer', SimpleImputer(strategy='median')),
    ('scaler', StandardScaler())
])

categorical_transformer = Pipeline([
    ('imputer', SimpleImputer(strategy='most_frequent')),
    ('onehot', OneHotEncoder(handle_unknown='ignore'))
])

# Combine transformers
preprocessor = ColumnTransformer([
    ('num', numeric_transformer, numeric_features),
    ('cat', categorical_transformer, categorical_features)
])

# Full pipeline
model = Pipeline([
    ('preprocessor', preprocessor),
    ('classifier', GradientBoostingClassifier(random_state=42))
])

# Fit and predict
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

Core Capabilities

Five capability areas are documented in references/core_capabilities.md, with per-topic detail in references/supervised_learning.md, references/unsupervised_learning.md, references/model_evaluation.md, references/preprocessing.md, and references/pipelines_and_composition.md:

1. Supervised learning — classification and regression estimator families.
2. Unsupervised learning — clustering, decomposition, and manifold learning.
3. Model evaluation and selection — metrics, cross-validation, and hyperparameter search.
4. Data preprocessing — scaling, encoding, imputation, and feature selection.
5. Pipelines and composition — Pipeline and ColumnTransformer.

Always fit preprocessing inside a Pipeline so it is refit per cross-validation fold; scaling or imputing before splitting leaks test information into training.

Two worked workflows are in references/common_workflows.md.

Example Scripts

Classification Pipeline

Run a complete classification workflow with preprocessing, model comparison, hyperparameter tuning, and evaluation:

uv run python scripts/classification_pipeline.py

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:

uv run python scripts/clustering_analysis.py

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

Reference Documentation

This skill includes comprehensive reference files for deep dives into specific topics:

Quick Reference

File: references/quick_reference.md

• Common import patterns and installation instructions
• Quick workflow templates for common tasks
• Algorithm selection cheat sheets
• Common patterns and gotchas
• Performance optimization tips

Supervised Learning

File: references/supervised_learning.md

• Linear models (regression and classification)
• Support Vector Machines
• Decision Trees and ensemble methods
• K-Nearest Neighbors, Naive Bayes, Neural Networks
• Algorithm selection guide

Unsupervised Learning

File: references/unsupervised_learning.md

• All clustering algorithms with parameters and use cases
• Dimensionality reduction techniques
• Outlier and novelty detection
• Gaussian Mixture Models
• Method selection guide

Model Evaluation

File: references/model_evaluation.md

• Cross-validation strategies
• Hyperparameter tuning methods
• Classification, regression, and clustering metrics
• Learning and validation curves
• Best practices for model selection

Preprocessing

File: references/preprocessing.md

• Feature scaling and normalization
• Encoding categorical variables
• Missing value imputation
• Feature engineering techniques
• Custom transformers

Pipelines and Composition

File: references/pipelines_and_composition.md

• Pipeline construction and usage
• ColumnTransformer for mixed data types
• FeatureUnion for parallel transformations
• Complete end-to-end examples
• Best practices

Best Practices

Always Use Pipelines

Pipelines prevent data leakage and ensure consistency:

# Good: Preprocessing in pipeline
pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('model', LogisticRegression())
])

# Bad: Preprocessing outside (can leak information)
X_scaled = StandardScaler().fit_transform(X)

Fit on Training Data Only

Never fit on test data:

# Good
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)  # Only transform

# Bad
scaler = StandardScaler()
X_all_scaled = scaler.fit_transform(np.vstack([X_train, X_test]))

Use Stratified Splitting for Classification

Preserve class distribution:

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, stratify=y, random_state=42
)

Set Random State for Reproducibility

model = RandomForestClassifier(n_estimators=100, random_state=42)

Choose Appropriate Metrics

• Balanced data: Accuracy, F1-score
• Imbalanced data: Precision, Recall, ROC AUC, Balanced Accuracy
• Cost-sensitive: Define custom scorer

Scale Features When Required

Algorithms requiring feature scaling:

• SVM, KNN, Neural Networks
• PCA, Linear/Logistic Regression with regularization
• K-Means clustering

Algorithms not requiring scaling:

• Tree-based models (Decision Trees, Random Forest, Gradient Boosting)
• Naive Bayes

Troubleshooting Common Issues

ConvergenceWarning

Issue: Model didn't converge Solution: Increase max_iter or scale features

model = LogisticRegression(max_iter=1000)

Poor Performance on Test Set

Issue: Overfitting Solution: Use regularization, cross-validation, or simpler model

# Add regularization
model = Ridge(alpha=1.0)

# Use cross-validation
scores = cross_val_score(model, X, y, cv=5)

Memory Error with Large Datasets

Solution: Use algorithms designed for large data

# Use SGD for large datasets
from sklearn.linear_model import SGDClassifier
model = SGDClassifier()

# Or MiniBatchKMeans for clustering
from sklearn.cluster import MiniBatchKMeans
model = MiniBatchKMeans(n_clusters=8, batch_size=100)

Additional Resources

• Official Documentation: https://scikit-learn.org/stable/
• User Guide: https://scikit-learn.org/stable/user_guide.html
• API Reference: https://scikit-learn.org/stable/api/index.html
• Examples Gallery: https://scikit-learn.org/stable/auto_examples/index.html