Predict on an H2O Model

Obtains predictions from various fitted H2O model objects.

# S3 method for H2OModel
predict(object, newdata, ...)

# S3 method for H2OModel
h2o.predict(object, newdata, ...)

Arguments

object	a fitted H2OModel object for which prediction is desired
newdata	An H2OFrame object in which to look for variables with which to predict.
...	additional arguments to pass on.

Value

Returns an H2OFrame object with probabilites and default predictions.

Details

This method dispatches on the type of H2O model to select the correct prediction/scoring algorithm. The order of the rows in the results is the same as the order in which the data was loaded, even if some rows fail (for example, due to missing values or unseen factor levels).

See also

h2o.deeplearning, h2o.gbm, h2o.glm, h2o.randomForest for model generation in h2o.

Examples

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

f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv"
insurance <- h2o.importFile(f)
predictors <- colnames(insurance)[1:4]
response <- "Claims"
insurance['Group'] <- as.factor(insurance['Group'])
insurance['Age'] <- as.factor(insurance['Age'])
splits <- h2o.splitFrame(data =  insurance, ratios = 0.8, seed = 1234)
train <- splits[[1]]
valid <- splits[[2]]
insurance_gbm <- h2o.gbm(x = predictors, y = response,
                         training_frame = train,
                         validation_frame = valid,
                         distribution = "huber",
                         huber_alpha = 0.9, seed = 1234)
h2o.predict(insurance_gbm, newdata = insurance)
}