`keep_cross_validation_fold_assignment`¶

Available in: GBM, DRF, Deep Learning, GLM, GAM, Naïve-Bayes, K-Means, XGBoost, AutoML
Hyperparameter: no

Description¶

When performing cross-validation, data is split into subsets using either the fold_column or fold_assignment parameter. You can then specify to save each of the outputted fold assignments by enabling the keep_cross_validation_fold_assignment option. Note that this option is disabled by default.

More information about cross-validation is available in the Cross-Validation section.

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 ` keep_cross_validation_fold_assignment` (boolean parameter):
# train your model, set nfolds parameter
cars_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train,
                    nfolds = 5,  keep_cross_validation_fold_assignment= TRUE, seed = 1234)

# retrieve the cross-validation fold assignment
h2o.cross_validation_fold_assignment(cars_gbm)

import h2o
from h2o.estimators.gbm import H2OGradientBoostingEstimator
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 using the ` keep_cross_validation_fold_assignment` (boolean parameter):
# first initialize your estimator, set nfolds parameter
cars_gbm = H2OGradientBoostingEstimator(keep_cross_validation_fold_assignment = True, nfolds = 5, seed = 1234)

# then train your model
cars_gbm.train(x = predictors, y = response, training_frame = train)

# retrieve the cross-validation fold assignment
cars_gbm.cross_validation_fold_assignment()