xgboostExplainer (version 0.1)

explainPredictions: Step 2: Get multiple prediction breakdowns from a trained xgboost model

Description

This function outputs the feature impact breakdown of a set of predictions made using an xgboost model.

Usage

explainPredictions(xgb.model, explainer, data)

Arguments

xgb.model

A trained xgboost model

explainer

The output from the buildExplainer function, for this model

data

A DMatrix of data to be explained

Value

A data table where each row is an observation in the data and each column is the impact of each feature on the prediction.

The sum of the row equals the prediction of the xgboost model for this observation (log-odds if binary response).

Examples

# NOT RUN {
library(xgboost)
library(xgboostExplainer)

set.seed(123)

data(agaricus.train, package='xgboost')

X = as.matrix(agaricus.train$data)
y = agaricus.train$label

train_idx = 1:5000

train.data = X[train_idx,]
test.data = X[-train_idx,]

xgb.train.data <- xgb.DMatrix(train.data, label = y[train_idx])
xgb.test.data <- xgb.DMatrix(test.data)

param <- list(objective = "binary:logistic")
xgb.model <- xgboost(param =param,  data = xgb.train.data, nrounds=3)

col_names = colnames(X)

pred.train = predict(xgb.model,X)
nodes.train = predict(xgb.model,X,predleaf =TRUE)
trees = xgb.model.dt.tree(col_names, model = xgb.model)

#### The XGBoost Explainer
explainer = buildExplainer(xgb.model,xgb.train.data, type="binary", base_score = 0.5, trees = NULL)
pred.breakdown = explainPredictions(xgb.model, explainer, xgb.test.data)

showWaterfall(xgb.model, explainer, xgb.test.data, test.data,  2, type = "binary")
showWaterfall(xgb.model, explainer, xgb.test.data, test.data,  8, type = "binary")
# }