Assign Data to One of n_folds Randomly and Produce Training/Validation Data Lists

Each row in the data are assigned to one of 1:n_folds at random. Then for each of i in 1:n_folds, the training_data[[i]] is comprised of the data with sl_fold != i, i.e., capturing roughly (n-folds-1)/n_folds proportion of the data. The validation data is a list of dataframes, each comprising of roughly 1/n_folds proportion of the data.

Usage

cv_random_schema(data, n_folds = 5)

Arguments

data: a data.frame (or similar) to split into training and validation datasets.
n_folds: The number of training_data and validation_data data frames to make.

Value

A list of two lists ($training_data and $validation_data) which are each lists of length n_folds. In each of those entries is a data.frame that contains the nth training or validation fold of the data.

Details

Since the assignment to folds is random, the proportions are not exact or guaranteed and there is some variability in the size of each training_data data frame, and likewise for the validation_data data frames.

Examples

data(Boston, package = "MASS")
training_validation_data <- cv_random_schema(Boston, n_folds = 3)
# take a look at what's in the output:
str(training_validation_data, max.level = 2)
#> List of 2
#>  $ training_data  :List of 3
#>   ..$ :'data.frame':	338 obs. of  14 variables:
#>   ..$ :'data.frame':	338 obs. of  14 variables:
#>   ..$ :'data.frame':	336 obs. of  14 variables:
#>  $ validation_data:List of 3
#>   ..$ :'data.frame':	168 obs. of  14 variables:
#>   ..$ :'data.frame':	168 obs. of  14 variables:
#>   ..$ :'data.frame':	170 obs. of  14 variables: