scikit-learn
The reference Python toolkit for classical machine learning.
scikit-learn covers classification, regression, clustering, dimensionality reduction, model selection and preprocessing, with a consistent fit/predict API across every algorithm. Built on NumPy and SciPy, it's the right choice for tabular data when deep learning is overkill and you want reliable models that are fast to train and simple to ship to production.
What scikit-learn brings to your project.
Typical use cases: Scoring, customer segmentation and prediction on tabular data.
- 01
Uniform fit/predict/transform API across dozens of algorithms.
- 02
Pipelines and cross-validation for a reproducible ML workflow.
- 03
Model selection: grid/random search, metrics, calibration.
- 04
Open source (BSD), outstanding docs, NumPy/SciPy ecosystem.
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