class_check: Evaluate the Accuracy of Outlier Classification Results

In HRTnomaly: Historical, Relational, and Tail Anomaly-Detection Algorithms

Evaluate the Accuracy of Outlier Classification Results

Description

The function computes the confusion matrix between the logical output of an outlier detection algorithm and a reference (ground-truth) logical vector. The function also calculates the overal accuracy of the results from the confusion matrix, including recall, precision, and F1-scores for the two classes (regular, versus outlier).

Usage

class_check(pred, truth)

Arguments

pred	A logical vector with the classification output from an anomaly detection algorithm.
truth	A logical vector with the observed classification as a reference (or ground truth).

Details

The function computes the confusion matrix using the function table. True positive and false negative are successively evaluated to compute overall accuracy, recall, precision, and F1-scores.

Value

An S3 class named checkwise with the confusion matrix, and other accuracy metrices appended as attribues.

attr(, "overall")

A numeric value between zero and one with the overall accuracy.

attr(, "recall")

A numeric vector of values between zero and one with the recall index for regular and outlier cells.

attr(, "precision")

A numeric vector of values between zero and one with the precision index for regular and outlier cells.

attr(, "f1-score")

A numeric vector of values between zero and one with the F1-scores for regular and outlier cells.

Author(s)

Luca Sartore [e-mail]

Examples

# Load the package
library(HRTnomaly)
set.seed(2025L)
# Load the 'toy' data
data(toy)
# Detect cellwise outliers using Bayesian Analysis
res <- cellwise(toy[sample.int(100), ], 0.5, 10L)
class_check(res$outlier, res$anomaly_flag != "")

HRTnomaly documentation built on April 3, 2025, 6:17 p.m.