/scikit-learn

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: "1.3"
  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`.

# 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](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:

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 algor