Scikit-learn Machine Learning

Scikit-learn is a free software machine learning library for the Python programming language, featuring classification, regression, clustering, dimensionality reduction, model selection, and preprocessing algorithms under a consistent API. Originally developed by David Cournapeau as a Google Summer of Code project in 2007 and now maintained by a community of over 3,000 contributors led by INRIA researchers including Gael Varoquaux and Andreas Mueller, the library has surpassed 60,000 citations in academic papers and is downloaded over 50 million times per month. The current version 1.9.0 requires Python 3.9 or later and builds on NumPy for array operations, SciPy for scientific computing, and matplotlib for visualization. Classification algorithms include Support Vector Machines with multiple kernel options, Random Forests and Gradient Boosting, k-Nearest Neighbors, Naive Bayes, Logistic Regression, and Decision Trees. Regression covers Linear Regression, Ridge, Lasso, ElasticNet, SVR, and ensemble methods. Clustering provides k-Means, DBSCAN, Mean Shift, Spectral Clustering, and Agglomerative Clustering. Dimensionality reduction includes Principal Component Analysis, t-SNE, UMAP integration, and Truncated SVD. The library also provides comprehensive tools for cross validation, hyperparameter tuning via GridSearchCV and RandomizedSearchCV, pipeline construction for reproducible workflows, feature extraction from text and images, and metrics for model evaluation including accuracy, precision, recall, F1 score, ROC AUC, and mean squared error.