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
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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))
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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)
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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: - Comparing model performance objectively - Finding optimal hyperparameters - Preventing overfitting through cross-validation - Understanding model behavior with learning curves

See: references/model_evaluation.md for comprehensive metrics and tuning strategies.

4. Data Preprocessing

Transform raw data into formats suitable for machine learning.

Scaling and normalization: - StandardScaler (zero mean, unit variance) - MinMaxScaler (bounded range) - RobustScaler (robust to outliers) - Normalizer (sample-wise normalization)

Encoding categorical variables: - OneHotEncoder (nominal categories) - OrdinalEncoder (ordered categories) - LabelEncoder (target encoding)

Handling missing values: - SimpleImputer (mean, median, most frequent) - KNNImputer (k-nearest neighbors) - IterativeImputer (multivariate imputation)

Feature engineering: - PolynomialFeatures (interaction terms) - KBinsDiscretizer (binning) - Feature selection (RFE, SelectKBest, SelectFromModel)

When to use: - Before training any algorithm that requires scaled features (SVM, KNN, Neural Networks) - Converting categorical variables to numeric format - Handling missing data systematically - Creating non-linear features for linear models

See: references/preprocessing.md for detailed preprocessing techniques.

5. Pipelines and Composition

Build reproducible, production-ready ML workflows.

Key components: - Pipeline: Chain transformers and estimators sequentially - ColumnTransformer: Apply different preprocessing to different columns - FeatureUnion: Combine multiple transformers in parallel - TransformedTargetRegressor: Transform target variable

Benefits: - Prevents data leakage in cross-validation - Simplifies code and improves maintainability - Enables joint hyperparameter tuning - Ensures consistency between training and prediction

When to use: - Always use Pipelines for production workflows - When mixing numerical and categorical features (use ColumnTransformer) - When performing cross-validation with preprocessing steps - When hyperparameter tuning includes preprocessing parameters

See: references/pipelines_and_composition.md for comprehensive pipeline patterns.

Example Scripts

Classification Pipeline

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

python scripts/classification_pipeline.py
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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:

python scripts/clustering_analysis.py
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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

Common Workflows

Building a Classification Model

1. Load and explore data python import pandas as pd df = pd.read_csv('data.csv') X = df.drop('target', axis=1) y = df['target']

2. Split data with stratification python from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, stratify=y, random_state=42 )

3. Create preprocessing pipeline ```python from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler from sklearn.compose import ColumnTransformer

# Handle numeric and categorical features separately preprocessor = ColumnTransformer([ ('num', StandardScaler(), numeric_features), ('cat', OneHotEncoder(), categorical_features) ]) ```

1. Build complete pipeline python model = Pipeline([ ('preprocessor', preprocessor), ('classifier', RandomForestClassifier(random_state=42)) ])

2. Tune hyperparameters ```python from sklearn.model_selection import GridSearchCV

param_grid = { 'classifier__n_estimators': [100, 200], 'classifier__max_depth': [10, 20, None] }

grid_search = GridSearchCV(model, param_grid, cv=5) grid_search.fit(X_train, y_train) ```

1. Evaluate on test set ```python from sklearn.metrics import classification_report

best_model = grid_search.best_estimator_ y_pred = best_model.predict(X_test) print(classification_report(y_test, y_pred)) ```

Performing Clustering Analysis

1. Preprocess data ```python from sklearn.preprocessing import StandardScaler

scaler = StandardScaler() X_scaled = scaler.fit_transform(X) ```

1. Find optimal number of clusters ```python from sklearn.cluster import KMeans from sklearn.metrics import silhouette_score

scores = [] for k in range(2, 11): kmeans = KMeans(n_clusters=k, random_state=42) labels = kmeans.fit_predict(X_scaled) scores.append(silhouette_score(X_scaled, labels))

optimal_k = range(2, 11)[np.argmax(scores)] ```

1. Apply clustering python model = KMeans(n_clusters=optimal_k, random_state=42) labels = model.fit_predict(X_scaled)

2. Visualize with dimensionality reduction ```python from sklearn.decomposition import PCA

pca = PCA(n_components=2) X_2d = pca.fit_transform(X_scaled)

plt.scatter(X_2d[:, 0], X_2d[:, 1], c=labels, cmap='viridis') ```

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)
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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]))
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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
)
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Set Random State for Reproducibility

model = RandomForestClassifier(n_estimators=100, random_state=42)
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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)
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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)
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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)
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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

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.