{"enrichment":{"faq":[{"a":"scikit-learn is Python's go-to library for classical machine learning across classification, regression, clustering, and dimensionality reduction. It includes algorithms ranging from linear models and decision trees to ensemble methods and support vector machines, plus comprehensive tools for preprocessing, model evaluation, and hyperparameter tuning.","q":"What is scikit-learn used for in machine learning?"},{"a":"scikit-learn enables you to create production-ready ML pipelines using Pipeline and ColumnTransformer to handle mixed data types. Combine preprocessing steps (scaling, encoding) with model estimators in a single workflow, ensuring consistent transformations during training and prediction while avoiding data leakage.","q":"How do I build a machine learning pipeline in Python with scikit-learn?"},{"a":"scikit-learn's GridSearchCV exhaustively searches over specified hyperparameter combinations, evaluating each with cross-validation. It automates the tuning process, tracks performance metrics, and returns the best parameters and model. Pair it with RandomizedSearchCV for faster exploration of larger parameter spaces.","q":"How does scikit-learn hyperparameter tuning with grid search work?"},{"a":"scikit-learn offers multiple clustering approaches: k-means for centroid-based partitioning, DBSCAN for density-based clustering that finds arbitrary shapes, hierarchical clustering for dendrograms, and Gaussian Mixture Models for probabilistic clustering. Choose based on your data structure and whether you need to specify cluster count upfront.","q":"What clustering algorithms does scikit-learn provide?"},{"a":"scikit-learn provides SimpleImputer for filling missing values (mean, median, most frequent), StandardScaler and MinMaxScaler for feature scaling, OneHotEncoder for categorical encoding, and RFE for feature selection. Combine these in a Pipeline to ensure reproducible, leak-free preprocessing workflows.","q":"How do I preprocess and handle missing values in scikit-learn?"},{"a":"scikit-learn includes cross_val_score for k-fold cross-validation, cross_validate for multiple metrics, and specialized scorers for classification (precision, recall, F1) and regression (R\u00b2, MSE). Use these to assess generalization, detect overfitting, and compare models fairly before hyperparameter tuning.","q":"What model evaluation and cross-validation tools does scikit-learn offer?"}],"shadow_tags":["supervised-learning","unsupervised-learning","model-tuning","data-transformation","ensemble-methods","feature-engineering","cross-validation","algorithm-selection","production-workflows","classical-ml"],"summary_rewrite":"scikit-learn is Python's go-to library for classical machine learning across classification, regression, clustering, and dimensionality reduction. It includes algorithms ranging from linear models and decision trees to ensemble methods and support vector machines, plus comprehensive tools for preprocessing, model evaluation, and hyperparameter tuning. Use it to build production-ready ML pipelines with built-in cross-validation and feature engineering capabilities."},"files":[{"bytes":15701,"path":"backend/cli/skills/coding/scikit-learn/SKILL.md","sha256":"ee575847033c8b3ac012529971f8fe4bd2c9d9eab6dcb6a013007198df9e0c8d","url":"https://skillfed.io/files/synthetic-sciences/openscience/scikit-learn/1e791fe5/SKILL.md"}],"id":"synthetic-sciences/openscience/scikit-learn","links":{"html":"https://skillfed.io/synthetic-sciences/openscience/scikit-learn","md":"https://skillfed.io/synthetic-sciences/openscience/scikit-learn.md","repo":"https://github.com/synthetic-sciences/openscience"},"meta":{"agents_supported":[],"first_seen":"2026-07-28","forks":403,"language":"TypeScript","last_updated":"2026-07-27","license":"Apache-2.0","name":"scikit-learn","publisher":"synthetic-sciences","stars":2896},"relations":{"similar":[{"id":"zLanqing/codex-claude-academic-skills/scikit-learn"},{"id":"LeonChaoX/qinyan-academic-skills/scikit-learn"},{"id":"foryourhealth111-pixel/Vibe-Skills/scikit-learn"},{"id":"drshailesh88/integrated_content_OS/scikit-learn"},{"id":"beita6969/ScienceClaw/scikit-learn"},{"id":"K-Dense-AI/scientific-agent-skills/scikit-learn"},{"id":"jaechang-hits/SciAgent-Skills/scikit-learn-machine-learning"},{"id":"tondevrel/scientific-agent-skills/scikit-learn"},{"id":"beita6969/ScienceClaw/scikit-learn-ml"},{"id":"pluginagentmarketplace/custom-plugin-data-engineer/machine-learning"}]},"slug":{"owner":"synthetic-sciences","repo":"openscience","skill":"scikit-learn"},"version":"1e791fe5"}