scorecard (version 0.4.4)

perf_cv: Cross Validation

Description

perf_cv provides cross validation on logistic regression and other binomial classification models.

Usage

perf_cv(dt, y, x = NULL, no_folds = 5, seeds = NULL,
  binomial_metric = "ks", positive = "bad|1", breaks_list = NULL, ...)

Value

A list of data frames of binomial metrics for each datasets.

Arguments

dt

A data frame with both x (predictor/feature) and y (response/label) variables.

y

Name of y variable.

x

Name of x variables. Defaults to NULL. If x is NULL, then all columns except y are counted as x variables.

no_folds

Number of folds for K-fold cross-validation. Defaults to 5.

seeds

The seeds to create multiple random splits of the input dataset into training and validation data by using split_df function. Defaults to NULL.

binomial_metric

Defaults to ks.

positive

Value of positive class, defaults to "bad|1".

breaks_list

List of break points, defaults to NULL. If it is NULL, then using original values of the input data to fitting model, otherwise converting into woe values based on training data.

...

Additional parameters.

Examples

if (FALSE) {
data("germancredit")

dt = var_filter(germancredit, y = 'creditability')
bins = woebin(dt, y = 'creditability')
dt_woe = woebin_ply(dt, bins)

perf1 = perf_cv(dt_woe, y = 'creditability', no_folds = 5)

perf2 = perf_cv(dt_woe, y = 'creditability', no_folds = 5,
   seeds = sample(1000, 10))

perf3 = perf_cv(dt_woe, y = 'creditability', no_folds = 5,
   binomial_metric = c('ks', 'auc'))

}