h2o.varimp_plot {h2o}

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 shown in the plot (default is 10 or all if less than 10).

See Also

h2o.std_coef_plot for GLM.

Examples

## Not run: 
library(h2o)
h2o.init()
prostate_path <- system.file("extdata", "prostate.csv", package = "h2o")
prostate <- h2o.importFile(prostate_path)
prostate[, 2] <- as.factor(prostate[, 2])
model <- h2o.gbm(x = 3:9, y = 2, training_frame = prostate, distribution = "bernoulli")
h2o.varimp_plot(model)

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

## End(Not run)