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

Targets scikit-learn 1.9.1, verified with Python 3.13. The release requires Python 3.11+; use its published wheels for your interpreter/platform. See the 1.9 release notes. The bundled scripts and regression tests are executable examples. Reference snippets using caller-provided X, y, columns, or placeholders are illustrative adaptations, not complete standalone programs.

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==1.9.1"

# Optional: plotting utilities and bundled script dependencies
uv pip install "scikit-learn[plots]==1.9.1" matplotlib pandas

# 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.

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