EstimationTask-class: Class '"EstimationTask"'

In performanceEstimation: An Infra-Structure for Performance Estimation of Predictive Models

Description

This class of objects contains the information describing an estimation task.

Details

In case you are providing your own user-defined evaluator functions (through parameters evaluator anbd evaluator.pars) you need to follow some protocol in defining these functions so that the package may correctly call them during the execution of the estimation experiments. This protocol depends on the output of the workflows you plan to evaluate with your use-defined function. Standard workflows (standardWF or timeseriesWF) will return at least a vector named trues with the true values of the test cases and the vector named preds with the respective predicted values (in this order). This means that your evaluator function should assume that it will be called with these two vectors as the first two arguments. However, if you are not using the standard workflows, you have more flexibility. In effect, user-defined workflows return whatever the author wants them to return (unless they are going to be evaluated using either classificationMetrics or regressionMetrics, that require the vectors of true and predicted values). This means that in the most flexible case where you have your own user-defined workflow function and your user-defined evaluator function, you can use whatever parameters you want. The only thing you need to worry is to be aware that your user-defined evaluator function will be called with whatever your user-defined workflow has returned as result of its execution. Your user-defined evaluator function should calculate whatever metrics are indicated through the parameter stats that is a vector of strings. In case the slot trainReq is TRUE then the user-defined evaluator function should also have a parameter named train.y that will "receive" the values of the target variable on the training set. The remaining parameters of the user-defined function can be freely defined by you and their values will be specified through the contents of the evaluator.pars list.

Objects from the Class

Objects can be created by calls of the form EstimationTask(...) providing the values for the class slots. These objects contain information on the metrics to be estimated, as well as on the estimation method to use to obtain the estimates. Moreover, in case you want to use metrics not currently implemented by this package you can also provide the name of a function (and its parameters) that will be called to calculate these metrics.

Slots

metrics:

An optional vector of objects of class character containing the names of the metrics to be estimated. These can be any of the metrics provided by the functions classificationMetrics and regressionMetrics or "trTime", "tsTime" or "totTime" for training, testing and total time, respectively. You may also provide the name of any other metrics, but in that case you need to use the slots evaluator and evaluator.pars to indicate the function to be use to calculate them. If you do not supply the name of any metric, all metrics of the used evaluator function will be calculated.

method:

Object of class EstimationMethod containing the estimation method and its settings to be used to obtain the estimates of the metrics (defaulting to CV()).

evaluator:

An optional object of class character containing the name of a function to be used to calculate the specified metrics. It will default to either classificationMetrics or regressionMetrics depending on the type of prediction task.

evaluator.pars:

An optional list containing the parameters to be passed to the function calculating the metrics.

trainReq:

An optional logical value indicating whether the metrics to be calculated require that the training data is supplied (defaulting to FALSE). Note that if the user selects any of the metrics "nmse", "nmae" or "theil" this will be set to TRUE.

Methods

show

signature(object = "EstimationTask"): method used to show the contents of a EstimationTask object.

Author(s)

Luis Torgo [e-mail]

References

Torgo, L. (2014) An Infra-Structure for Performance Estimation and Experimental Comparison of Predictive Models in R. arXiv:1412.0436 [cs.MS] http://arxiv.org/abs/1412.0436

See Also

MonteCarlo, CV, LOOCV, Bootstrap, Holdout, EstimationMethod

Examples

showClass("EstimationTask")

## Estimating Mean Squared Error using 10-fold cross validation
et <- EstimationTask(metrics="mse")
et

## Estimating Accuracy and Total Time (train+test times) using 3
## repetitions of holdout with 20% of the cases used for testing. 
EstimationTask(metrics=c("acc","totTime"),method=Holdout(nReps=3,hldSz=0.2))

## An example with a user-defined metric: the average differences between true
## predicted values raised to a certain power.

## First we define the function that calculates this metric. It
## needs to have 'trues' and 'preds' as the first two arguments if we
## want it to be usable by standard workflows
powErr <- function(trues,preds,metrics,pow=3) {
     if (metrics != "pow.err") stop("Unable to calculate that metric!")
     c(pow.err = mean((trues-preds)^pow))
}

## Now the estimation task (10-fold cv in this example)
EstimationTask(metrics="pow.err", method=CV(), 
               evaluator="powErr", evaluator.pars=list(pow=4))

performanceEstimation documentation built on May 2, 2019, 6:01 a.m.