evaluation_metrics: Binary Classification Evaluation

In MixtureMissing: Robust and Flexible Model-Based Clustering for Data Sets with Missing Values at Random

Binary Classification Evaluation

Description

Evaluate the performance of a classification model by comparing its predicted labels to the true labels. Various metrics are returned to give an insight on how well the model classifies the observations. This function is added to aid outlier detection evaluation of MCNM and MtM in case that true outliers are known in advance.

Usage

evaluation_metrics(true_labels, pred_labels)

Arguments

true_labels	An 0-1 or logical vector denoting the true labels. The meaning of 0 and 1 (or TRUE and FALSE) is up to the user.
pred_labels	An 0-1 or logical vector denoting the true labels. The meaning of 0 and 1 (or TRUE and FALSE) is up to the user.

Value

A list with the following slots:

matr	The confusion matrix built upon true labels and predicted labels.
TN	True negative.
FP	False positive (type I error).
FN	False negative (type II error).
TP	True positive.
TPR	True positive rate (sensitivy).
FPR	False positive rate.
TNR	True negative rate (specificity).
FNR	False negative rate.
precision	Precision or positive predictive value (PPV).
accuracy	Accuracy.
error_rate	Error rate.
FDR	False discovery rate.

Examples

#++++ Inputs are 0-1 vectors ++++#

evaluation_metrics(
  true_labels = c(1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1),
  pred_labels = c(1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1)
)

#++++ Inputs are logical vectors ++++#

evaluation_metrics(
  true_labels = c(TRUE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE),
  pred_labels = c(FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, FALSE)
)

MixtureMissing documentation built on Oct. 16, 2024, 1:09 a.m.