fold_column

  • Available in: GBM, DRF, Deep Learning, GLM, GAM, Naïve-Bayes, K-Means, AutoML, XGBoost

  • Hyperparameter: no

Description

When performing N-fold cross validation, you can specify to split the data into subsets using a fold_assignment. A fold assignment is suitable for datasets that are i.i.d.

If your dataset requires custom grouping to perform meaningful cross-validation, then a “fold column” should be created and provided instead. The fold_column option specifies the column in the dataset that contains the cross-validation fold index assignment per observation. The fold column can include integers (0, 1, 2, … , N-1 values or 1, 2, 3… , N values) or categorical values. When specified, the algorithm uses that column’s values to split the data into subsets.

Notes

  • The fold column must exist in the training data.

  • The fold column that is specified cannot be specified in ignored_columns, response_colum, weights_column or offset_column.

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"

# create a fold column with 5 folds
# randomly assign fold numbers 0 through 4 for each row in the column
fold_numbers <- h2o.kfold_column(cars, nfolds=5)

# rename the column "fold_numbers"
names(fold_numbers) <- "fold_numbers"

# print the fold_assignment column
print(fold_numbers)

# append the fold_numbers column to the cars dataset
cars <- h2o.cbind(cars,fold_numbers)

# try using the fold_column parameter:
cars_gbm <- h2o.gbm(x = predictors, y = response, training_frame = cars,
                    fold_column="fold_numbers", seed = 1234)

# print the auc for your model
print(h2o.auc(cars_gbm, xval = TRUE))

import h2o
from h2o.estimators.gbm import H2OGradientBoostingEstimator
h2o.init()
h2o.cluster().show_status()

# 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"

# create a fold column with 5 folds
# randomly assign fold numbers 0 through 4 for each row in the column
fold_numbers = cars.kfold_column(n_folds = 5, seed = 1234)

# rename the column "fold_numbers"
fold_numbers.set_names(["fold_numbers"])

# append the fold_numbers column to the cars dataset
cars = cars.cbind(fold_numbers)

# print the fold_assignment column
print(cars['fold_numbers'])

# initialize the estimator then train the model
cars_gbm = H2OGradientBoostingEstimator(seed = 1234)
cars_gbm.train(x=predictors, y=response, training_frame=cars, fold_column="fold_numbers")

# print the auc for the cross-validated data
cars_gbm.auc(xval=True)