evaluation.precision: Precision of classification predictions

In fdm2id: Data Mining and R Programming for Beginners

Precision of classification predictions

Description

Evaluation predictions of a classification model according to precision. Works only for two classes problems.

Usage

evaluation.precision(predictions, gt, positive = levels(gt)[1], ...)

Arguments

predictions	The predictions of a classification model (factor or vector).
gt	The ground truth (factor or vector).
positive	The label of the positive class.
...	Other parameters.

Value

The evaluation of the predictions (numeric value).

See Also

evaluation.accuracy, evaluation.fmeasure, evaluation.fowlkesmallows, evaluation.goodness, evaluation.jaccard, evaluation.kappa, evaluation.recall,evaluation

Examples

require (datasets)
data (iris)
d = iris
levels (d [, 5]) = c ("+", "+", "-") # Building a two classes dataset
d = splitdata (d, 5)
model.nb = NB (d$train.x, d$train.y)
pred.nb = predict (model.nb, d$test.x)
evaluation.precision (pred.nb, d$test.y)

fdm2id documentation built on July 9, 2023, 6:05 p.m.