The Best AutoSKLearn Alternatives in 2026

modAL provides a modular active learning framework for Python built on top of scikit-learn.

Feature Engine provides Python tools for feature engineering and selection that are compatible with scikit-learn.

nilearn provides Python tools for machine learning and neuroimaging analysis.

A Python package to assess and improve fairness of machine learning models.

Python package which helps to debug machine learning classifiers and explain their predictions.

An open source Python library for drift detection in machine learning systems.

The Featuretools Python library supports automated feature engineering and is published as open source.

pytorch tabular provides a unified framework for deep learning models on tabular data.

The lightly Python library supports self supervised learning on images.

PyCaret provides a low code AutoML platform for Python, with a scikit-learn native engine and React control plane.

NALP covers Natural Adversarial Language Processing through a dedicated library.

neuropredict evaluates predictive performance using neuroimaging features.

AutoViz automatically creates visualizations for datasets of different sizes from a single line of code.

tsfresh automatically extracts relevant features from time series data.

Snorkel helps generate training data programmatically through weak supervision.

PySS3 supports interpretable text classification with SS3 and visualization tools for explainable AI.

A tool to help you configure, organize, log and reproduce experiments.

AIF360 provides fairness metrics and explanations for datasets and models, along with algorithms to mitigate bias.

Ultralytics supports detection, segmentation, image classification, pose estimation, and tracking across YOLO model versions.

Little Ball of Fur provides graph sampling extensions for NetworKit and NetworkX.

This TabGAN repository reviews research on applying GANs to generate tabular data.

19 Questions assigns attributes to objects using machine learning and Bayesian inference, according to its project description.

oneDAL is a C++ and DPC++ library for accelerated machine learning routines on tabular data, governed by the UXL Foundation.

InterpretML fits interpretable models and explains results from black-box machine learning systems.