Plot a GLM model's standardized coefficient magnitudes.
h2o.std_coef_plot(model, num_of_features = NULL)
model | A trained generalized linear model |
---|---|
num_of_features | The number of features to be shown in the plot |
h2o.varimp_plot
for variable importances plot of
random forest, GBM, deep learning.
# 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) # }