Description Usage Arguments Value Examples

This function allows to create (un)stratified folds from a label vector.

1 |

`y` |
Type: The label vector. |

`k` |
Type: integer. The amount of folds to create. Causes issues if |

`stratified` |
Type: boolean. Whether the folds should be stratified (keep the same label proportions) or not. Defaults to |

`seed` |
Type: integer. The seed for the random number generator. Defaults to |

`named` |
Type: boolean. Whether the folds should be named. Defaults to |

A list of vectors for each fold, where an integer represents the row number.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | ```
# Reproducible Stratified folds
data <- 1:5000
folds1 <- kfold(y = data, k = 5, stratified = TRUE, seed = 111)
folds2 <- kfold(y = data, k = 5, stratified = TRUE, seed = 111)
identical(folds1, folds2)
# Stratified Regression
data <- 1:5000
folds <- kfold(y = data, k = 5, stratified = TRUE)
for (i in 1:length(folds)) {
print(mean(data[folds[[i]]]))
}
# Stratified Multi-class Classification
data <- c(rep(0, 250), rep(1, 250), rep(2, 250))
folds <- kfold(y = data, k = 5, stratified = TRUE)
for (i in 1:length(folds)) {
print(mean(data[folds[[i]]]))
}
# Unstratified Regression
data <- 1:5000
folds <- kfold(y = data, k = 5, stratified = FALSE)
for (i in 1:length(folds)) {
print(mean(data[folds[[i]]]))
}
# Unstratified Multi-class Classification
data <- c(rep(0, 250), rep(1, 250), rep(2, 250))
folds <- kfold(y = data, k = 5, stratified = FALSE)
for (i in 1:length(folds)) {
print(mean(data[folds[[i]]]))
}
``` |

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