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evaluation.precision

Precision of classification predictions


Description

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

Usage

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

Arguments

predictions

The predictions of a classification model (factor or vector).

targets

Actual targets of the dataset (factor or vector).

positive

The label of the positive class.

...

Other parameters.

Value

The evaluation of the predictions (numeric value).

See Also

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

Data Mining and R Programming for Beginners

v0.9.5
GPL-3
Authors
Alexandre Blansché [aut, cre]
Initial release

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