README.md

In richardangell/GbmExplainR: Decompose gbm predictions into feature contributions + bias

README

GbmExplainR

GbmExplainR is a package to decompose gbm predictions into feature contributions. There is also functionality to plot individual trees from the models and the route for a given observation through the tree to a terminal node. GbmExplainR works with the gbm package.

GbmExplainR is based off the treeinterpreter Python package, there a blog post on treeinterpreter here.

Decompose gbm predictions into feature contributions

Let's look at the predicted value from a gbm. Note this model is the first example from ?gbm.

predict(gbm1, data[1, ], n.trees = gbm1$n.trees)

## [1] 1.575326

For a given prediction from a gbm, the feature contributions can be extracted;

decompose_gbm_prediction(gbm = gbm1, prediction_row = data[1, ])

##   variable contribution variable_value  variable_class
## 1       X3  -1.48439005              d ordered, factor
## 2       X2   0.44671068       1.061618         numeric
## 3       X1  -0.25693447      0.2655087         numeric
## 4       X4   0.13191849              e          factor
## 5       X6  -0.03038647      0.1064986         numeric
## 6       X5  -0.02561677              a          factor
## 7     Bias   2.79402449           <NA>              NA

Notice how the feature contributions sum to give the predicted value. These can be charted with a simple barchart;

plot_feature_contributions(feature_contributions = decompose_gbm_prediction(gbm1, data[1, ]),
                           cex.names = 0.8)

Tree structure and terminal node path

Individual trees can be plotted, and the route to a terminal node can be highlighted for a given observation;

plot_tree(gbm = gbm1, 
          tree_no = 1, 
          plot_path = data[1, ], 
          edge.label.cex = 1.2,
          vertex.label.cex = 1.2) 

Installation

Install form Github with devtools;

library(devtools)
devtools::install_github(richardangell/GbmExplainR)

Other similar works

There are other similar packages in R and Python that implement the same method for a variety of tree based models;

• treeinterpreter Python package implements prediction decomposition for decision tree, random forests and extra tree models in scikit-learn
• eli5 Python package contains an explain_predictions function to do prediction decomposition for various models
• xgboostExplainer implements prediction decomposition for xgboost models from R

richardangell/GbmExplainR documentation built on May 22, 2019, 12:54 p.m.