SHAP library [2] creates explanations of the model wondering for every prediction and feature how the prediction *x* change if feature *y* is removed from the model. The so-called **SHAP values** are the answer.

Since we built LTR models using LambdaMART (Multiple Additive Regression Trees), we used the **TreeExplainer** [3], an algorithm to compute SHAP values for trees and ensembles of trees, in polynomial time.

TreeSHAP provides us with several different types of plots, each one highlighting a specific aspect of the model. **Matplotlib**, a highly useful visualization library, is used for the rendering of the graphs in Python.

###### SUMMARY PLOT

The summary plot gives us **Global Interpretability**. The **shap.summary_plot** function with plot_type = “**bar**”, let you produce the feature importance plot (variables ranked in descending order) with the mean(|SHAP value|) in the x-axis.