sample_stratified: Stratified Random Sampling

In daltoolbox: Leveraging Experiment Lines to Data Analytics

Stratified Random Sampling

Description

The sample_stratified function in R is used to generate a stratified random sample from a given dataset. Stratified sampling is a statistical method that is used when the population is divided into non-overlapping subgroups or strata, and a sample is selected from each stratum to represent the entire population. In stratified sampling, the sample is selected in such a way that it is representative of the entire population and the variability within each stratum is minimized.

Usage

sample_stratified(attribute)

Arguments

attribute

attribute target to model building

Value

returns an object of class sample_stratified

Examples

#using stratified sampling
sample <- sample_stratified("Species")
tt <- train_test(sample, iris)

# distribution of train
table(tt$train$Species)

# preparing dataset into four folds
folds <- k_fold(sample, iris, 4)

# distribution of folds
tbl <- NULL
for (f in folds) {
 tbl <- rbind(tbl, table(f$Species))
}
head(tbl)

daltoolbox documentation built on Nov. 3, 2024, 9:06 a.m.