# TreeSHAP Gradient Boosted Trees (Regression)

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 Gradient Boosted Trees (Regression) Predictor is used as a substitute to the Gradient Boosted Trees Predictor (Regression). Simply replace every Gradient Boosted Trees 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.".

#### Credits

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

## Options

Change prediction column name
Select if you want to change the name of the column containing the prediction.
Prediction column name
The name of the column that will contain the prediction of the tree ensemble model
Show explanation
Activate this to compute the SHAP values. If this box is unchecked, the node is equivalent to a simple predictor node.
Compute interactions
Computes the Shapley interaction values exactly. WARNING: Computationally expensive. The runtime increases by 2 * #features compared to the SHAP values without interactions.

## Input Ports

The output of the Gradient Boosted Trees Learner (Regression).
Data to be predicted and explained.

## Output Ports

The input data along with prediction columns and corresponding SHAP values.

## Views

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