---
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.
metadata:
  version: "1.2"
  skill-author: K-Dense Inc.
---

# 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`.

```bash
# 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:

```python
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

```python
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

```python
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](references/core_capabilities.md), with per-topic detail
in [references/supervised_learning.md](references/supervised_learning.md),
[references/unsupervised_learning.md](references/unsupervised_learning.md),
[references/model_evaluation.md](references/model_evaluation.md),
[references/preprocessing.md](references/preprocessing.md), and
[references/pipelines_and_composition.md](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](references/common_workflows.md).

## Example Scripts

### Classification Pipeline

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

```bash
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:

```bash
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:
```python
# 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:
```python
# 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:
```python
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
```python
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
```python
model = LogisticRegression(max_iter=1000)
```

### Poor Performance on Test Set
**Issue:** Overfitting
**Solution:** Use regularization, cross-validation, or simpler model
```python
# 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
```python
# 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