| evaluation.fmeasure | R Documentation |
Evaluation predictions of a classification model according to the F-measure index.
evaluation.fmeasure(predictions, gt, beta = 1, positive = levels(gt)[1], ...)
predictions |
The predictions of a classification model ( |
gt |
The ground truth ( |
beta |
The weight given to precision. |
positive |
The label of the positive class. |
... |
Other parameters. |
The evaluation of the predictions (numeric value).
evaluation.accuracy, evaluation.fowlkesmallows, evaluation.goodness, evaluation.jaccard, evaluation.kappa, evaluation.precision,
evaluation.precision, evaluation.recall,
evaluation
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$test.x)
evaluation.fmeasure (pred.nb, d$test.y)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.