The Best Pycaret Alternatives in 2026

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

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.

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

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

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

Python library for decision tree visualization and model interpretation.

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.

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

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

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

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

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

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

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

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.

DrivenData hosts data science competitions where participants build predictive models for organizations addressing social challenges.

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