The Best Fairlearn Alternatives in 2026

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

nilearn provides Python tools for machine learning and neuroimaging analysis.

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

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

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

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

The lightly Python library supports self supervised learning on images.

Automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.

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

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

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

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

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.

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

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

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

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

Cleanlab provides a Python library for data quality and machine learning with messy real world data and labels.

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

Manifold provides visual debugging for machine learning models without depending on a specific model type.