scikit-learnlisted

# 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 ```bash # 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 ```python 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 ) # Pr