fdm2id (version 0.9.6)

performance: Performance estimation

Description

Estimate the performance of classification or regression methods using bootstrap or crossvalidation (accuracy, ROC curves, confusion matrices, ...)

Usage

performance(
  methods,
  train.x,
  train.y,
  test.x = NULL,
  test.y = NULL,
  train.size = round(0.7 * nrow(train.x)),
  type = c("evaluation", "confusion", "roc", "cost", "scatter"),
  protocol = c("bootstrap", "crossvalidation", "loocv", "holdout", "train"),
  eval = ifelse(is.factor(train.y), "accuracy", "r2"),
  nruns = 10,
  nfolds = 10,
  new = TRUE,
  lty = 1,
  seed = NULL,
  methodparameters = NULL,
  names = NULL,
  ...
)

Value

The evaluation of the predictions (numeric value).

Arguments

methods

The classification or regression methods to be evaluated.

train.x

The dataset (description/predictors), a matrix or data.frame.

train.y

The target (class labels or numeric values), a factor or vector.

test.x

The test dataset (description/predictors), a matrix or data.frame.

test.y

The (test) target (class labels or numeric values), a factor or vector.

train.size

The size of the training set (holdout estimation).

type

The type of evaluation (confusion matrix, ROC curve, ...)

protocol

The evaluation protocol (crossvalidation, bootstrap, ...)

eval

The evaluation functions.

nruns

The number of bootstrap runs.

nfolds

The number of folds (crossvalidation estimation).

new

A logical value indicating whether a new plot should be be created or not (cost curves or ROC curves).

lty

The line type (and color) specified as an integer (cost curves or ROC curves).

seed

A specified seed for random number generation (useful for testing different method with the same bootstap samplings).

methodparameters

Method parameters (if null tuning is done by cross-validation).

names

Method names.

...

Other specific parameters for the leaning method.

See Also

confusion, evaluation, cost.curves, roc.curves

Examples

if (FALSE) {
require ("datasets")
data (iris)
# One method, one evaluation criterion, bootstrap estimation
performance (NB, iris [, -5], iris [, 5], seed = 0)
# One method, two evaluation criteria, train set estimation
performance (NB, iris [, -5], iris [, 5], eval = c ("accuracy", "kappa"),
             protocol = "train", seed = 0)
# Three methods, ROC curves, LOOCV estimation
performance (c (NB, LDA, LR), linsep [, -3], linsep [, 3], type = "roc",
             protocol = "loocv", seed = 0)
# List of methods in a variable, confusion matrix, hodout estimation
classif = c (NB, LDA, LR)
performance (classif, iris [, -5], iris [, 5], type = "confusion",
             protocol = "holdout", seed = 0, names = c ("NB", "LDA", "LR"))
# List of strings (method names), scatterplot evaluation, crossvalidation estimation
classif = c ("NB", "LDA", "LR")
performance (classif, iris [, -5], iris [, 5], type = "scatter",
             protocol = "crossvalidation", seed = 0)
}