Scikit Learn

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 uvuv uv pip install scikit-learn
# Optional: Install visualization dependenciesuv uv pip install matplotlib seaborn
# Commonly used withuv 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_splitfrom sklearn.preprocessing import StandardScalerfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.metrics import classification_report
# Split dataX_train, X_test, y_train, y_test = train_test_split(    X, y, test_size=0.2, stratify=y, random_state=42)
# Preprocessscaler = StandardScaler()X_train_scaled = scaler.fit_transform(X_train)X_test_scaled = scaler.transform(X_test)
# Train modelmodel = RandomForestClassifier(n_estimators=100, random_state=42)model.fit(X_train_scaled, y_train)
# Evaluatey_pred = model.predict(X_test_scaled)print(classification_report(y_test, y_pred))

Complete Pipeline with Mixed Data

from sklearn.pipeline import Pipelinefrom sklearn.compose import ColumnTransformerfrom sklearn.preprocessing import StandardScaler, OneHotEncoderfrom sklearn.impute import SimpleImputerfrom sklearn.ensemble import GradientBoostingClassifier
# Define feature typesnumeric_features = ['age', 'income']categorical_features = ['gender', 'occupation']
# Create preprocessing pipelinesnumeric_transformer = Pipeline([    ('imputer', SimpleImputer(strategy='median')),    ('scaler', StandardScaler())])
categorical_transformer = Pipeline([    ('imputer', SimpleImputer(strategy='most_frequent')),    ('onehot', OneHotEncoder(handle_unknown='ignore'))])
# Combine transformerspreprocessor = ColumnTransformer([    ('num', numeric_transformer, numeric_features),    ('cat', categorical_transformer, categorical_features)])
# Full pipelinemodel = Pipeline([    ('preprocessor', preprocessor),    ('classifier', GradientBoostingClassifier(random_state=42))])
# Fit and predictmodel.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

1. Load and explore data

   python

   import pandas as pddf = 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_splitX_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 Pipelinefrom sklearn.preprocessing import StandardScalerfrom sklearn.compose import ColumnTransformer
   # Handle numeric and categorical features separatelypreprocessor = ColumnTransformer([    ('num', StandardScaler(), numeric_features),    ('cat', OneHotEncoder(), categorical_features)])

4. Build complete pipeline

   python

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

5. 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)

6. 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)

2. Find optimal number of clusters

   python

   from sklearn.cluster import KMeansfrom 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)]

3. Apply clustering

   python

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

4. 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 pipelinepipeline = 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:

# Goodscaler = StandardScaler()X_train_scaled = scaler.fit_transform(X_train)X_test_scaled = scaler.transform(X_test)  # Only transform
# Badscaler = 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 regularizationmodel = Ridge(alpha=1.0)
# Use cross-validationscores = cross_val_score(model, X, y, cv=5)

Memory Error with Large Datasets

Solution: Use algorithms designed for large data

# Use SGD for large datasetsfrom sklearn.linear_model import SGDClassifiermodel = SGDClassifier()
# Or MiniBatchKMeans for clusteringfrom sklearn.cluster import MiniBatchKMeansmodel = 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