InterpretML

What it is

InterpretML implements the Explainable Boosting Machine (EBM) and provides visualization tools for EBMs, other glass-box models, and black-box explanations. It is ideal for developers and data scientists who need fully interpretable machine learning models based on Generalized Additive Models (GAMs).

InterpretML stands out by providing fully interpretable machine learning models and visualization tools, making it ideal for applications that require transparent decision-making processes.

When it fits — and when it doesn’t

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

1. 01

   Performance limitations with large datasets

   InterpretML can be slow when processing very large datasets due to its interpretability requirements

What it does well

• Explainable Boosting Machine (EBM) implementation

• Visualization tools for EBMs and other models

• Interpretability of machine learning models based on GAMs

Pricing

Free tier

YesOpen source — free to use

Paid from

$0

Model

Flat rate

Enterprise plan

No

Pricing not independently confirmed — check the vendor’s page.