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 an