fdm2id (version 0.9.6)

evaluation: Evaluation of classification or regression predictions

Description

Evaluation predictions of a classification or a regression model.

Usage

evaluation(
  predictions,
  gt,
  eval = ifelse(is.factor(gt), "accuracy", "r2"),
  ...
)

Value

The evaluation of the predictions (numeric value).

Arguments

predictions

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

gt

The ground truth of the dataset (factor or vector).

eval

The evaluation method.

...

Other parameters.

See Also

confusion, evaluation.accuracy, evaluation.fmeasure, evaluation.fowlkesmallows, evaluation.goodness, evaluation.jaccard, evaluation.kappa, evaluation.precision, evaluation.recall, evaluation.msep, evaluation.r2, performance

Examples

require (datasets)
data (iris)
d = splitdata (iris, 5)
model.nb = NB (d$train.x, d$train.y)
pred.nb = predict (model.nb, d$test.x)
# Default evaluation for classification
evaluation (pred.nb, d$test.y)
# Evaluation with two criteria
evaluation (pred.nb, d$test.y, eval = c ("accuracy", "kappa"))
data (trees)
d = splitdata (trees, 3)
model.linreg = LINREG (d$train.x, d$train.y)
pred.linreg = predict (model.linreg, d$test.x)
# Default evaluation for regression
evaluation (pred.linreg, d$test.y)