A result metric (RM) that calculates confusion matrices

This result metric calculate a confusion matrices from all points in time.

rm_confusion_matrix(
  ndr_container_or_object = NULL,
  save_TCD_results = FALSE,
  create_decision_vals_confusion_matrix = TRUE
)

Arguments

ndr_container_or_object

The purpose of this argument is to make the constructor of the rm_confusion_matrix feature preprocessor work with the magrittr pipe (|>) operator. This argument should almost never be directly set by the user to anything other than NULL. If this is set to the default value of NULL, then the constructor will return a rm_confusion_matrix object. If this is set to an ndr container, then a rm_confusion_matrix object will be added to the container and the container will be returned. If this argument is set to another ndr object, then both that ndr object as well as a new rm_confusion_matrix object will be added to a new container and the container will be returned.

save_TCD_results

A Boolean specifying whether one wants to save results to allow one to create temporal cross decoding confusion matrices; i.e., confusion matrices when training at one point in time and testing a different point in time. Setting this to FALSE can save memory.

create_decision_vals_confusion_matrix

A boolean specifying whether one wants to create a confusion matrix of the decision values. In this confusion matrix, each row corresponds to the correct class (like a regular confusion matrix) and each column corresponds to the mean decision value of the predictions for each class.

Value

This constructor creates an NDR result metric object with the class rm_confusion_matrix. Like all NDR result metric objects, this result metric will be used by a cross-validator to create a measure of decoding accuracy by aggregating the results after all cross-validation splits have been run, and after all resample runs have completed.

Details

Like all result metrics, this result metric has functions to aggregate results after completing each set of cross-validation classifications, and also after completing all the resample runs. The results should then be available in the DECODING_RESULTS object returned by the cross-validator.

See also

Other result_metrics: plot.rm_confusion_matrix(), plot.rm_main_results(), plot_main_results(), rm_main_results()

Examples

# If you only want to use the rm_confusion_matrix(), then you can put it in a
# list by itself and pass it to the cross-validator.
the_rms <- list(rm_confusion_matrix())