CrossValidateSensitivityToNorm: Cross validates models to predict sensitivity to...

In sevvandi/outselect: Algorithm selection for unsupervised outlier detection

Description

Cross validates models to predict sensitivity to normalization.

Usage

CrossValidateSensitivityToNorm(rocpr = 1, xi = 0.05, n = 10)

Arguments

rocpr	If rocpr=1 then area under ROC curve is used as the performance measure. If rocpr=2 area under PR curve is used.
xi	The xi-sensitivity to normalization parameter xi. Defaults to 0.05
n	The number of folds in cross validation.

Value

A list containing the following results from the cross validated models:

def_acc

The default accuracy we get if we predict the method is not good for all instances. This is the percentage of the majority class.

results

The n-fold cross valdation results.

mean_acc

The mean n-fold cross valdation results.

Examples

## Not run: 
out <- CrossValidateSensitivityToNorm(1,0.05,10)
out$mean_acc

## End(Not run)

sevvandi/outselect documentation built on June 1, 2019, 3:58 a.m.