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NodeNode / Predictor

TreeSHAP Random Forest (Regression)

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SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions. While SHAP can explain the output of any machine learning model, Lundberg and his collaborators have developed a high-speed exact algorithm for tree ensemble methods [1] , [2] .

Usage

The Tree SHAP Random Forest (Regression) Predictor is used as a substitute to the Random Forest Predictor (Regression). Simply replace every Random Forest Predictor (Regression) with this node to get started. If you are using a different tree based method, consider the other nodes in this package.

Interpretation

The beautiful thing about SHAP values is the intuitive interpretation. Every model has an expected output, the average prediction. The model prediction for a data row is the expected output plus the summation of SHAP values. This leads to intuitive explanations, for example in revenue forecasts for customers "The high interaction of the customer with the product website over the last three months contributed 1200 euros to the predicted revenue next year.".

Enterprise Support

If you need help integrating explainable machine learning methods in your company, please contact me at morriskurz@gmail.com

Credits

All credits to the original research and development of the C++ and Python code go to Lundberg and his collaborators.

Node details

Input ports
  1. Type: Tree Ensembles
    Random Forest Model
    The output of the Tree Ensemble Learner (Regression).
  2. Type: Table
    Input data
    Data to be predicted and explained.
Output ports
  1. Type: Table
    Explanation output
    The input data along with prediction columns and corresponding SHAP values.

Extension

The TreeSHAP Random Forest (Regression) node is part of this extension:

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Related workflows & nodes

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