SHAP

What it is

SHAP provides a unified measure of feature importance by applying game theory. It helps in understanding how each feature contributes to the prediction for individual data points, making it crucial for transparent and interpretable AI systems.

SHAP stands out by providing a rigorous, game-theoretic approach to explain model predictions, ensuring that each feature's contribution is fairly attributed and understood.

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

   Limited performance with large datasets

   SHAP computations can become computationally expensive and slow for high-dimensional data

2. 02

   Complex setup for integrating with existing ML pipelines

   Requires manual integration steps to work seamlessly with various frameworks like TensorFlow, PyTorch, etc.

What it does well

• Game theoretic approach to explain model outputs

• Unified measure of feature importance

• Supports a wide range of machine learning models

• Provides both global and local explanations

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.