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Data science tools

SHAP

Explains why an AI model made a particular prediction by showing how much each input pushed the result.

Widely used

What it does

SHAP stands for SHapley Additive exPlanations. It uses an idea from game theory, a branch of maths about sharing credit fairly, to work out how much each piece of input data contributed to a model's answer.

For example, if a model predicts a house price, SHAP can show which details pushed the price up and which pulled it down. It draws charts that make these explanations easy to see.

It works with many kinds of machine-learning models, with extra-fast methods for popular tree-based tools.

Who it's for

Data scientists and researchers who need to understand, check or explain their models' decisions.

Can I use it without coding?

Developers only. It's a Python library used in code. New to this? Start here.

Can I run it?

Cost
FreeNothing to pay or sign up for.
API key
Not needed

Try it

Using Claude Code, Cursor or another AI coding assistant? Paste the prompt and it will walk you through the setup.New to this?

Before you install

  • Only download from the official GitHub page. Copies with similar names exist for many popular projects.
  • License (MIT): Very permissive: free to use, change and share, including for business.
  • More on staying safe

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