plot.importance: Plot importance measures

Description Usage Arguments Details Value Examples

Description

This functions plots selected measures of importance for variables and interactions. It is possible to visualise importance table in two ways: radar plot with six measures and scatter plot with two choosen measures.

Usage

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## S3 method for class 'importance'
plot(x, ..., top = 10, radar = TRUE,
  text_start_point = 0.5, text_size = 3.5, xmeasure = "sumCover",
  ymeasure = "sumGain")

Arguments

x

a result from the importance function.

...

other parameters.

top

number of positions on the plot or NULL for all variable. Default 10.

radar

TRUE/FALSE. If TRUE the plot shows six measures of variables' or interactions' importance in the model. If FALSE the plot containing two chosen measures of variables' or interactions' importance in the model.

text_start_point

place, where the names of the particular feature start. Available for 'radar=TRUE'. Range from 0 to 1. Default 0.5.

text_size

size of the text on the plot. Default 3.5.

xmeasure

measure on the x-axis.Available for 'radar=FALSE'. Default "sumCover".

ymeasure

measure on the y-axis. Available for 'radar=FALSE'. Default "sumGain".

Details

Available measures:

Additionally for plots with single variables:

Value

a ggplot object

Examples

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library("EIX")
library("Matrix")
sm <- sparse.model.matrix(left ~ . - 1,  data = HR_data)

library("xgboost")
param <- list(objective = "binary:logistic", max_depth = 2)
xgb_model <- xgboost(sm, params = param, label = HR_data[, left] == 1, nrounds = 25, verbose=0)

imp <- importance(xgb_model, sm, option = "both")
imp
plot(imp,  top = 10)

imp <- importance(xgb_model, sm, option = "variables")
imp
plot(imp,  top = nrow(imp))

 imp <- importance(xgb_model, sm, option = "interactions")
 imp
plot(imp,  top =  nrow(imp))

 imp <- importance(xgb_model, sm, option = "variables")
 imp
plot(imp, top = NULL, radar = FALSE, xmeasure = "sumCover", ymeasure = "sumGain")


library(lightgbm)
train_data <- lgb.Dataset(sm, label =  HR_data[, left] == 1)
params <- list(objective = "binary", max_depth = 2)
lgb_model <- lgb.train(params, train_data, 25)

imp <- importance(lgb_model, sm, option = "both")
imp
plot(imp,  top = nrow(imp))

imp <- importance(lgb_model, sm, option = "variables")
imp
plot(imp, top = NULL, radar = FALSE, xmeasure = "sumCover", ymeasure = "sumGain")

EIX documentation built on May 31, 2019, 5:04 p.m.