The Best InterpretML Alternatives in 2026

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.

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

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

DVCLive logs and tracks machine learning metrics, parameters, and models with Git or DVC.

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

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

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

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

nilearn provides Python tools for machine learning and neuroimaging analysis.

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

This Swift library builds machine learning predictions using linear regression.

LIME provides explanations for predictions made by machine learning classifiers.

MCGrad calibrates machine learning predictions across the full dataset and across segments defined by input features.

PredictionBuilder builds machine learning predictions using linear regression.

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

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.

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.

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

Python library for decision tree visualization and model interpretation.

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