/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.

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

# Install scikit-learn using uv
uv uv pip install scikit-learn

# Optional: Install visualization dependencies
uv uv pip install matplotlib seaborn

# Commonly used with
uv uv pip install pandas numpy

When to Use This Skill

Use the scikit-learn skill when:

• Building classification or regression models
• Running ordinary regression analysis where the main need is fitting, coefficients, confidence intervals, residual checks, or cross-validation rather than causal identification
• 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

Do not use this skill for causal identification, policy-effect claims, DiD, synthetic control, or interrupted time-series effect estimation; those belong to `performing-causal-analysis`.

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

1. Supervised Learning

Comprehensive algorithms for classification and regression tasks.

**Key algorithms:**

• **Linear models**: Logistic Regression, Linear Regression, Ridge, Lasso, ElasticNet
• **Tree-based**: Decision Trees, Random Forest, Gradient Boosting
• **Support Vector Machines**: SVC, SVR with various kernels
• **Ensemble methods**: AdaBoost, Voting, Stacking
• **Neural Networks**: MLPClassifier, MLPRegressor
• **Others**: Naive Bayes, K-Nearest Neighbors

**When to use:**

• Classification: Predicting discrete categories (spam detection, image classification, fraud detection)
• Regression: Predicting continuous values (price prediction, demand forecasting)

**See:** `references/supervised_learning.md` for detailed algorithm documentation, parameters, and usage examples.

2. Unsupervised Learning

Discover patterns in unlabeled data through clustering and dimensionality reduction.

**Clustering algorithms:**

• **Partition-based**: K-Means, MiniBatchKMeans
• **Density-based**: DBSCAN, HDBSCAN, OPTICS
• **Hierarchical**: AgglomerativeClustering
• **Probabilistic**: Gaussian Mixture Models
• **Others**: MeanShift, SpectralClustering, BIRCH

**Dimensionality reduction:**

• **Linear**: PCA, TruncatedSVD, NMF
• **Manifold learning**: t-SNE, UMAP, Isomap, LLE
• **Feature extraction**: FastICA, LatentDirichletAllocation

**When to use:**

• Customer segmentation, anomaly detection, data visualization
• Reducing feature dimensions, exploratory data analysis
• Topic modeling, image compression

**See:** `references/unsupervised_learning.md` for detailed documentation.

3. Model Evaluation and Selection

Tools for robust model evaluation, cross-validation, and hyperparameter tuning.

**Cross-validation strategies:**

• KFold, StratifiedKFold (classification)
• TimeSeriesSplit (temporal data)
• GroupKFold (grouped samples)

**Hyperparameter tuning:**

• GridSearchCV (exhaustive search)
• RandomizedSearchCV (random sampling)
• HalvingGridSearchCV (successive halving)

**Metrics:**

• **Classification**: accuracy, precision, recall, F1-score, ROC AUC, confusion matrix
• **Regression**: MSE, RMSE, MAE, R², MAPE
• **Clustering**: silhouette score, Calinski-Harabasz, Davies-Bouldin

**When to use:** -