EIX: Titanic data "

In EIX: Explain Interactions in 'XGBoost'

knitr::opts_chunk$set(fig.height = 6, fig.width = 6, fig.align = "center")

Data Info

This vignette shows usage of EIX package for titanic data. This dataset was copied from stablelearner package. With EIX package we explain XGBoost classification model concerning the survival problem. More details about EIX package here.

#devtools :: install_github("ModelOriented/EIX")
library("EIX")
library(data.table)
set.seed(4)
titanic_data<-data.table(na.omit(titanic_data))
knitr::kable(head(titanic_data))

library("Matrix")
sparse_matrix <- sparse.model.matrix(survived ~ . - 1,  data = titanic_data)

Xgboost model creation

library("xgboost")
param <- list(objective = "binary:logistic", max_depth = 2)
xgb_model <- xgboost(sparse_matrix, params = param, label = titanic_data[, "survived"] == "yes", nrounds = 50, verbose = FALSE)

Model visualization

First let's plot the model.

lolli<-lollipop(xgb_model,sparse_matrix)
plot(lolli, threshold=0.02)

Interactions

Next we explore interactions using interactions() functions and its plot.

interactions<-interactions(xgb_model, sparse_matrix, option = "interactions")
head(interactions, 15)
plot(interactions)

Variables' and interactions’ importance

importance<-importance(xgb_model, sparse_matrix, option = "both")
head(importance, 15)
plot(importance, radar=FALSE)

plot(importance)

Explanation of the single prediction including interactions

Let's see an explanation of the prediction for an 18-year-old from England who has traveled 3rd class.

data <- titanic_data[27,]
new_observation <- sparse_matrix[27,]
wf<-waterfall(xgb_model, new_observation, data, option = "interactions")
wf
plot(wf)

EIX documentation built on March 23, 2021, 9:06 a.m.