scikit-learn is an open-source Python library for classic machine learning, such as classification, regression and clustering, with one consistent way to train models.
In plain words
Models in scikit-learn, called estimators, are trained with fit and then used with predict. That shared pattern makes it easy to swap one algorithm for another. It also provides tools to prepare data, choose models and measure how well they work, and pipelines that chain these steps so test data does not leak into training.
A quick example
Imagine predicting whether an email is spam from a table of features. You scale the numbers, train a logistic regression model with fit, then call predict on new emails, and later swap in a random forest by changing one line.
Where you'll meet it
- Teaching and learning machine learning.
- Everyday models on tabular data in industry and research.
Related
Sources
- DocsGetting Started, scikit-learn · read 29 Sept 2026
- DocsAbout us, scikit-learn · read 29 Sept 2026
Checked by WhatIsAI against the sources above · 29 Sept 2026. Spotted an error? Report it.