# score_each_iteration¶

• Available in: GBM, DRF, Deep Learning, GLM, PCA, GLRM, Naïve-Bayes, K-Means
• Hyperparameter: no

## Description¶

This option allows you to specify to score during each iteration of model training. This option is useful when used with early stopping and attempting to make early stopping reproducible. When used with early stopping, the stopping_rounds option applies to the number of scoring iterations that H2O has performed, so regular scoring iterations of small size help control early stopping the most (though there is a speed tradeoff to scoring more often). The default is to use H2O’s assessment of a reasonable ratio of training iterations to scoring time, which often results in inconsistent scoring gaps.

This option is disabled by default.

## Example¶

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 sets
cars.split <- h2o.splitFrame(data = cars,ratios = 0.8, seed = 1234)
train <- cars.split[[1]]
valid <- cars.split[[2]]

# try using the score_each parameter (boolean parameter):
# set ntrees = 55 and print out score for all 55 trees
cars_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train,
validation_frame = valid, score_each_iteration = TRUE,
ntrees = 55, seed = 1234)

# print the auc for your model
print(h2o.scoreHistory(cars_gbm))

import 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.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], seed = 1234)

# try turning on the score_each_iteration parameter (boolean parameter):