confusionMatrix.nlcv: compute a confusion matrix for the optimal number of features...

In nlcv: Nested Loop Cross Validation

Description

The observed and predicted classes are cross-tabulated for a given classification technique used in the nested loop cross validation. The predicted class that is used to construct the confusion matrix is the class that was predicted most of the time (>= 50%) across all runs of the nested loop.

Usage

## S3 method for class 'nlcv'
confusionMatrix(x, tech, proportions = TRUE, ...)

Arguments

x	object for which a confusionMatrix should be produced, e.g. one produced by the nlcv function; for the print method, it is the object to be printed
tech	string indicating the classification technique for which the confusion matrix should be returned
proportions	logical indicating whether the cells of the matrix should contain proportions (TRUE) or raw counts (FALSE)
...	Dots argument to pass additional parameters to the confusionMatrix or print methods

Value

confusionMatrix produces an object of class confusionMatrix which directly inherits from the ftable class (representing the confusion matrix)

Author(s)

Willem Talloen and Tobias Verbeke

nlcv documentation built on May 2, 2019, 7:28 a.m.