performance {fdm2id} | R Documentation |
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", "avsp"),
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,
...
)
Arguments
methods |
The classification or regression methods to be evaluated. |
train.x |
The dataset (description/predictors), a |
train.y |
The target (class labels or numeric values), a |
test.x |
The test dataset (description/predictors), a |
test.y |
The (test) target (class labels or numeric values), a |
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. |
Value
The evaluation of the predictions (numeric value).
See Also
confusion
, evaluation
, cost.curves
, roc.curves
Examples
## Not run:
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)
# Actual vs. predicted
data (trees)
performance (LINREG, trees [, -3], trees [, 3], type = "avsp")
## End(Not run)