Goodness
Evaluation predictions of a classification model according to Goodness index.
evaluation.goodness( predictions, targets, beta = 1, positive = levels(targets)[1], ... )
predictions |
The predictions of a classification model ( |
targets |
Actual targets of the dataset ( |
beta |
The weight given to precision. |
positive |
The label of the positive class. |
... |
Other parameters. |
The evaluation of the predictions (numeric value).
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$test.x)
evaluation.goodness (pred.nb, d$test.y)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.