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()

prosPath <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex <- h2o.importFile(prosPath)
prostate.hex[,2] <- as.factor(prostate.hex[,2])
prostate.glm <- h2o.glm(y = "CAPSULE", x = c("AGE","RACE","PSA","DCAPS"),
                         training_frame = prostate.hex, family = "binomial",
                         nfolds = 0, alpha = 0.5, lambda_search = FALSE)
h2o.std_coef_plot(prostate.glm)
# }