Scikit-learn Skill | Agent Skills

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:

Supervised learning — classification and regression estimator families.
Unsupervised learning — clustering, decomposition, and manifold learning.
Model evaluation and selection — metrics, cross-validation, and hyperparameter search.
Data preprocessing — scaling, encoding, imputation, and feature selection.
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

Agent Skills: Scikit-learn

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