h2o (version 3.10.3.6)

h2o.varimp_plot: Plot Variable Importances

Description

Plot Variable Importances

Usage

h2o.varimp_plot(model, num_of_features = NULL)

Arguments

model

A trained model (accepts a trained random forest, GBM, or deep learning model, will use h2o.std_coef_plot for a trained GLM

num_of_features

The number of features to be shown in the plot

See Also

h2o.std_coef_plot for GLM.

Examples

library(h2o)
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
hex <- h2o.importFile(prosPath)
hex[,2] <- as.factor(hex[,2])
model <- h2o.gbm(x = 3:9, y = 2, training_frame = hex, distribution = "bernoulli")
h2o.varimp_plot(model)

# for deep learning set the variable_importance parameter to TRUE
iris.hex <- as.h2o(iris)
iris.dl <- h2o.deeplearning(x = 1:4, y = 5, training_frame = iris.hex,
variable_importances = TRUE)
h2o.varimp_plot(iris.dl)

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