scikit-learn

Topics in the source

• Scikit-learn
• Overview
• Installation
• When to Use This Skill
• Quick Start
• Classification Example
• Complete Pipeline with Mixed Data
• Core Capabilities

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. 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 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-Ne

This is an excerpt. Refer to the complete source and its current requirements.