pcic: Proportion of Incorrectly Classified Cases

In brandon-mosqueda/SKM: Sparse Kernels Methods

Proportion of Incorrectly Classified Cases

Description

Given the observed and predicted values of categorical data (of any number of classes) computes the Proportion of Incorrectly Classified Cases (1 - pccc(), the same as: 1 - accuracy).

Usage

pcic(observed, predicted, remove_na = TRUE)

Arguments

observed	(factor) The observed values. It has to have the same length as predicted.
predicted	(factor) The observed values. It has to have the same length as observed.
remove_na	(logical(1)) Should NA values be removed?. TRUE by default.

Value

A single numeric value with the Proportion of Incorrectly Classified Cases.

See Also

Other categorical_metrics: accuracy(), brier_score(), categorical_summary(), confusion_matrix(), f1_score(), kappa_coeff(), math_mode(), matthews_coeff(), pccc(), pr_auc(), precision(), recall(), roc_auc(), sensitivity(), specificity()

Examples

## Not run: 
pccc(c("a", "b"), c("a", "b"))
pccc(c("a", "b"), c("b", "a"))
pccc(c("a", "b"), c("b", "b"))
pccc(c("a", "b", "a"), c("b", "a", "c"))

## End(Not run)

brandon-mosqueda/SKM documentation built on Feb. 8, 2025, 5:24 p.m.