evaluation: Evaluation of classification or regression predictions

In fdm2id: Data Mining and R Programming for Beginners

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"),
  ...
)

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.

Value

The evaluation of the predictions (numeric value).

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)

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