h2o.varimp_plot | R Documentation |
Plot Variable Importances
h2o.varimp_plot(model, num_of_features = NULL)
model |
A trained model (accepts a trained random forest, GBM,
or deep learning model, will use |
num_of_features |
The number of features shown in the plot (default is 10 or all if less than 10). |
h2o.std_coef_plot
for GLM.
## 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)
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