predict.classifierOutput {MLInterfaces} | R Documentation

## Predict method for `classifierOutput` objects

### Description

This function predicts values based on models trained with MLInterfaces' `MLearn` interface to many machine learning algorithms.

### Usage

    ## S3 method for class 'classifierOutput'
    predict(object, newdata, ...)

### Arguments

`object` | An instance of class `classifierOutput`.
`newdata` | An object containing the new input data: either a `matrix`, a `data.frame` or an `ExpressionSet`.
`...` | Other arguments to be passed to the algorithm-specific predict methods.

### Details

This S3 method will extract the ML model from the `classifierOutput` instance and call either a generic predict method or, if available, a specficly written wrapper to do classes prediction and class probabilities.

### Value

Currently, a list with

`testPredictions` | A factor with class predictions.
`testScores` | A `numeric` or `matrix` with class probabilities.

### Note

The function output will most likely be updated in a near future to a `classifierOutput` (or similar) object.

### Author(s)

Laurent Gatto <lg390@cam.ac.uk>

### See Also

`MLearn` and `classifierOutput`.

### Examples

    ## Not run:
    set.seed(1234)
    data(sample.ExpressionSet)
    trainInd <- 1:16

    clout.svm <- MLearn(type~., sample.ExpressionSet[100:250,], svmI, trainInd)
    predict(clout.svm, sample.ExpressionSet[100:250,-trainInd])

    clout.ksvm <- MLearn(type~., sample.ExpressionSet[100:250,], ksvmI, trainInd)
    predict(clout.ksvm, sample.ExpressionSet[100:250,-trainInd])

    clout.nnet <- MLearn(type~., sample.ExpressionSet[100:250,], nnetI, trainInd, size=3, decay=.01 )
    predict(clout.nnet, sample.ExpressionSet[100:250,-trainInd])

    clout.knn <- MLearn(type~., sample.ExpressionSet[100:250,], knnI(k=3), trainInd)
    predict(clout.knn, sample.ExpressionSet[100:250,-trainInd],k=1)
    predict(clout.knn, sample.ExpressionSet[100:250,-trainInd],k=3)

    #clout.plsda <- MLearn(type~., sample.ExpressionSet[100:250,], plsdaI, trainInd)
    #predict(clout.plsda, sample.ExpressionSet[100:250,-trainInd])

    clout.nb <- MLearn(type~., sample.ExpressionSet[100:250,], naiveBayesI, trainInd)
    predict(clout.nb, sample.ExpressionSet[100:250,-trainInd])

    # this can fail if training set does not yield sufficient diversity in response vector;
    # setting seed seems to help with this example, but other applications may have problems
    #
    clout.rf <- MLearn(type~., sample.ExpressionSet[100:250,], randomForestI, trainInd)
    predict(clout.rf, sample.ExpressionSet[100:250,-trainInd])

    ## End(Not run) # end of dontrun

[Package MLInterfaces version 1.67.10 Index]