TreeSHAP Random Forest

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 Predictor is used as a substitute to the Random Forest Predictor. Simply replace every Random Forest Predictor 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 predictive maintenance "The high production output over the last three months contributed +20% probability that the machine breaks down in the next month.".

Enterprise Support

If you need help integrating explainable machine learning methods in your company, please contact me at [e-mail]

Credits

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

Input ports

1. Type: Tree Ensembles
   Random Forest Model
   The output of the Random Forest Learner. Remember to activate the option "Save target distribution in tree nodes" in the learner node.
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.