Obtains predictions from various fitted H2O model objects.
# S3 method for H2OModel predict(object, newdata, ...) # S3 method for H2OModel h2o.predict(object, newdata, ...)
| 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. |
Returns an H2OFrame object with probabilites and default predictions.
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).
h2o.deeplearning, h2o.gbm,
h2o.glm, h2o.randomForest for model
generation in h2o.
# NOT RUN { 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) # }