scikit-learn/scikit-learn

Health v2 · maintenance only

Adoption not part of the score

67065 stars · 27318 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

scikit-learn is a Python module for machine learning built on top of SciPy, providing classification, regression, clustering, and model evaluation tools. It is a mature, BSD-licensed open-source project maintained by a large community since 2007.

Use cases

• train a classifier on tabular data
• fit a regression model in Python
• cluster unlabeled data
• evaluate model performance with cross-validation
• preprocess and scale features for ML
• build a machine learning pipeline

When to choose

• you need classical ML (not deep learning) in Python
• you want a well-documented, stable, widely supported library
• you need consistent APIs for preprocessing, training, and evaluation

When to avoid

• you need GPU-accelerated deep learning (use PyTorch or TensorFlow)
• you need distributed training at massive scale (use Spark MLlib)
• you need the latest research models not yet implemented

Facets

library · maturity stable

machine-learning data-science math machine-learning data-science python cross-platform classification regression clustering model-evaluation numpy scipy algorithms

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