scikit-learn

What it does

scikit-learn is a Python machine learning library organized around common tasks: classification (identifying which category an object belongs to, such as spam detection or image recognition), regression (predicting a continuous value, such as drug response or stock prices), and clustering (grouping similar objects, such as customer segments). It also covers dimensionality reduction, model selection, and preprocessing of inputs such as text.

Users call these tasks from Python code, with named algorithms for each: gradient boosting, nearest neighbors, random forest and logistic regression for classification; k-Means, HDBSCAN and hierarchical clustering for grouping; PCA and non-negative matrix factorization for reducing variables; and grid search and cross validation for tuning models.

It suits developers and data practitioners who build machine learning pipelines in Python. It is a library installed into a Python environment rather than a hosted service, so licensing, supported platforms and version requirements should be checked in the project's installation documentation before adoption.

What it can do

• Classification with algorithms such as gradient boosting, nearest neighbors, random forest, and logistic regression
• Regression with algorithms such as gradient boosting, nearest neighbors, random forest, and ridge
• Clustering with k-Means, HDBSCAN, and hierarchical clustering
• Dimensionality reduction with PCA, feature selection, and non-negative matrix factorization
• Model selection with grid search, cross validation, and metrics
• Preprocessing and feature extraction for transforming input data such as text

Before you choose

• Licensing and cost terms are not stated on the main documentation page; check the project's source repository for terms.
• It is a code library with no hosted interface, so a Python environment and coding skill are required.
• Supported platforms and versions are listed in the install documentation; verify them before relying on a specific setup.

Listed under

• Data
• Classification
• Clustering
• Data science
• Machine learning
• Open source
• Python library

Get started

Follow the installation guide, then the getting started guide.

1. Open the install guide at http://scikit-learn.org/stable/install.html and install the library into your Python environment.
2. Read the getting started guide at http://scikit-learn.org/stable/getting_started.html.
3. Choose a task (classification, regression, clustering, dimensionality reduction, model selection, or preprocessing) and call the matching algorithm.

Licensing and cost terms should be verified in the project's repository.

We couldn’t find published prices for scikit-learn.

Sources for this overview

Product pages, documentation, and pricing checked 29 Oct 2026. Each source supports part of the overview; availability and prices can change.

• scikit-learn: machine learning in Python — scikit-learn 1.9.1 documentation ↗scikit-learn.org

Company website

scikit-learn.org: product, pricing and policy pages

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Last checked 29 Oct 2026. Every field keeps its source and date; unknown means we couldn’t confirm it.