evaluateModelPerformance {LedPred} | R Documentation

## Evaluate model performances

### Description

`evaluateModelPerformance` function computes the precision and recall measures to evaluate the model through cross validation steps using `ROCR` package.

### Usage

    evaluateModelPerformance(data, cl = 1, valid.times = 10,
      feature.ranking = NULL, feature.nb = NULL,
      numcores = ifelse(.Platform$OS.type == "windows", 1, parallel::detectCores()
      - 1), file.prefix = NULL, kernel = "linear", cost = NULL,
      gamma = NULL)

### Arguments

`data` | data.frame containing the training set
`cl` | integer indicating the column number corresponding to the response vector that classify positive and negative regions (default = 1)
`valid.times` | Integer indicating how many times the training set will be split for the cross validation step (default = 10). This number must be smaller than positive and negative sets sizes.
`feature.ranking` | List of ordered features.
`feature.nb` | the optimal number of feature to use from the list of ordered features.
`numcores` | Number of cores to use for parallel computing (default: the number of available cores in the machine - 1)
`file.prefix` | A character string that will be used as a prefix followed by "_ROCR_perf.png" for the result plot file, if it is NULL (default), no plot is returned
`kernel` | SVM kernel, a character string: "linear" or "radial". (default = "radial")
`cost` | The SVM cost parameter for both linear and radial kernels. If NULL (default), the function `mcTune` is run.
`gamma` | The SVM gamma parameter for radial kernel. If radial kernel and NULL (default), the function `mcTune` is run.

### Value

A list with two objects.

`probs` | The predictions computed by the model for each subset during the cross-validation
`labels` | The actual class for each subset

### Examples

    data(crm.features)
    data(feature.ranking)
    #probs.labels.list <- evaluateModelPerformance(data.granges=crm.features,
    #    feature.ranking=feature.ranking, feature.nb=50,
    #    file.prefix = "test")
    #names(probs.labels.list[[1]])

[Package LedPred version 1.20.0 Index]