early_stopping

  • Available in: GLM, GAM

  • 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.

Example

R

Python

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 = 0.8)
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))

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)