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

• 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

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

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

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

Load and explore data

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

Split data with stratification

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 )

Create preprocessing pipeline

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) ])

Build complete pipeline

model = Pipeline([ ('preprocessor', preprocessor), ('classifier', RandomForestClassifier(random_state=42)) ])

Tune hyperparameters

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)

Evaluate on test set

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

Preprocess data

from sklearn.preprocessing import StandardScaler

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

Find optimal number of clusters

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)]

Apply clustering

model = KMeans(n_clusters=optimal_k, random_state=42) labels = model.fit_predict(X_scaled)

Visualize with dimensionality reduction

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)

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