f1_score: F1 score

In brandon-mosqueda/SKM: Sparse Kernels Methods

F1 score

Description

Given the observed and predicted values of categorical data (of any number of classes) computes the F1 score, that combines the precision and recall, and it is defined as the harmonic mean of the precision and recall.

Usage

f1_score(observed, predicted, positive_class = NULL, 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.
positive_class	(character(1)) The name of the class (level) to be taken as reference as the positive class. This parameter is only used for binary variables. NULL by default which uses the second class in the union of the classes (levels) in observed and predicted.
remove_na	(logical(1)) Should NA values be removed?. TRUE by default.

Details

F1 score is computed as:

See precision() and recall() for more information.

Value

For binary data a single value is returned, for more than 2 categories a vector of F1 scores is returned, one per each category.

See Also

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

Examples

## Not run: 
f1_score(factor(c("a", "b")), factor(c("a", "b")))
f1_score(factor(c("a", "b", "a", "b")), factor(c("a", "b", "b", "a")))
f1_score(factor(c("a", "b")), factor(c("b", "b")))

## End(Not run)

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