``early_stopping`` ------------------ - Available in: GLM - Hyperparameter: no Description ~~~~~~~~~~~ The ``early_stopping`` option specifies whether to stop early when there is no more relative improvement on the training or validation (if provided) set. This option prevents expensive model building with many predictors when no more improvements are occurring. This option is enabled by default. Related Parameters ~~~~~~~~~~~~~~~~~~ - None Example ~~~~~~~ .. example-code:: .. code-block:: r library(h2o) h2o.init() # import the cars dataset: # this dataset is used to classify whether or not a car is economical based on # the car's displacement, power, weight, and acceleration, and the year it was made cars <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") # convert response column to a factor cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"]) # set the predictor names and the response column name predictors <- c("displacement","power","weight","acceleration","year") response <- "economy_20mpg" # split into train and validation cars.splits <- h2o.splitFrame(data = cars, ratios = .8) train <- cars.splits[[1]] valid <- cars.splits[[2]] # try using the `early_stopping` parameter: car_glm <- h2o.glm(x = predictors, y = response, family = 'binomial', training_frame = train, validation_frame = valid, early_stopping = TRUE) # print the auc for your validation data print(h2o.auc(car_glm, valid = TRUE)) .. code-block:: python import h2o from h2o.estimators.glm import H2OGeneralizedLinearEstimator h2o.init() # import the cars dataset: # this dataset is used to classify whether or not a car is economical based on # the car's displacement, power, weight, and acceleration, and the year it was made cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") # convert response column to a factor cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() # set the predictor names and the response column name predictors = ["displacement","power","weight","acceleration","year"] response = "economy_20mpg" # split into train and validation sets train, valid = cars.split_frame(ratios = [.8]) # try using the `early_stopping` parameter: # Initialize and train a GLM cars_glm = H2OGeneralizedLinearEstimator(family = 'binomial', early_stopping = True) cars_glm.train(x = predictors, y = response, training_frame = train, validation_frame = valid) # print the auc for the validation data cars_glm.auc(valid = True)