The Best Sacred Alternatives in 2026

NALP covers Natural Adversarial Language Processing through a dedicated library.

neuropredict evaluates predictive performance using neuroimaging features.

nilearn provides Python tools for machine learning and neuroimaging analysis.

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

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

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

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

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.

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

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.

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

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

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.

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

The lightly Python library supports self supervised learning on images.

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

DVC tracks data versions and machine learning experiments for collaborative development.

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

H2O AutoML automatically generates features and selections, with a no code AutoML offering.

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