perf_cv: Cross Validation

In ShichenXie/scorecard: Credit Risk Scorecard

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, ...)

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.

Value

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

Examples

## Not run: 
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'))


## End(Not run)

ShichenXie/scorecard documentation built on April 17, 2024, 8:55 p.m.