Return coefficients fitted on the standardized data (requires standardize = True, which is on by default). These coefficients can be used to evaluate variable importance.

h2o.coef_norm(object, predictorSize = -1)

Arguments

object	an H2OModel object.
predictorSize	predictor subset size. If specified, will only return model coefficients of that subset size. If not specified will return a lists of model coefficient dicts for all predictor subset size.

Examples

if (FALSE) {
library(h2o)
h2o.init()

f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv"
cars <- h2o.importFile(f)
predictors <- c("displacement", "power", "weight", "acceleration", "year")
response <- "cylinders"
cars_split <- h2o.splitFrame(data = cars, ratios = 0.8, seed = 1234)
train <- cars_split[[1]]
valid <- cars_split[[2]]
cars_glm <- h2o.glm(balance_classes = TRUE,
                    seed = 1234,
                    x = predictors,
                    y = response,
                    training_frame = train,
                    validation_frame = valid)
h2o.coef_norm(cars_glm)
}