The Best TSFresh Alternatives in 2026

Snorkel helps generate training data programmatically through weak supervision.

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

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

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

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

mljar-supervised provides tabular data AutoML with feature engineering, hyperparameter tuning, explanations, and documentation.

This project profiles data quality and supports exploratory analysis of Pandas and Spark DataFrames.

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

Lance provides an open lakehouse format for multimodal AI, with vector indexing and data versioning.

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

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

Python library for decision tree visualization and model interpretation.

Modin scales Pandas data workflows by changing a single line of code in a project.

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

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

Optimus supports agile data preparation workflows across Pandas, Dask, cuDF, Vaex, and PySpark.

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

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.

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

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

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

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