keep_cross_validation_fold_assignment
¶
- Available in: GBM, DRF, Deep Learning, GLM, 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()