This website is using cookies to ensure you get the best experience possible on our website.
More info: Privacy & Cookies, Imprint
Scikit-Learn is one of the most popular Python libraries for machine learning. It provides an extensive collection of algorithms and tools for data analysis and machine learning models, including supervised and unsupervised learning, dimensionality reduction, and model selection.
Scikit-Learn provides an easy-to-use API that allows developers to create and train machine learning models quickly and easily. It is also tightly coupled with other Python libraries such as NumPy, SciPy, and Pandas, and provides a variety of tools for data manipulation, visualization, and preprocessing.
Supported algorithms in Scikit-Learn include linear and logistic regression, decision tree, random forest, k-nearest neighbor, naive Bayes, and support vector machine (SVM). It also provides model validation and optimization features, including cross-validation, grid and randomized search, and pipelines.
Scikit-Learn is widely used in science, industry, and academic research and is one of the most popular machine learning libraries in Python.