Plot a GLM model's standardized coefficient magnitudes.

h2o.std_coef_plot(model, num_of_features = NULL)

Arguments

model

A trained generalized linear model

num_of_features

The number of features to be shown in the plot

See also

h2o.varimp_plot for variable importances plot of random forest, GBM, deep learning.

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])
prostate_glm <- h2o.glm(y = "CAPSULE", x = c("AGE", "RACE", "PSA", "DCAPS"),
                         training_frame = prostate, family = "binomial",
                         nfolds = 0, alpha = 0.5, lambda_search = FALSE)
h2o.std_coef_plot(prostate_glm)
# }