The Best modAL Alternatives in 2026

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

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.

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 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.

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

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.

The LightGBM framework uses gradient boosting for ranking, classification, and other machine learning tasks.

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

Spektral provides a Python framework for creating graph neural networks with Keras and TensorFlow.