classifierOutput-class {MLInterfaces} | R Documentation

## Class "classifierOutput"

### Description

This class summarizes the output values from different classifiers.

### Objects from the Class

Objects are typically created during the application of a supervised machine learning algorithm to data and are the value returned. It is very unlikely that any user would create such an object by hand.

### Slots

`testOutcomes`:

  Object of class `"factor"` that lists the actual outcomes in the records on the test set

`testPredictions`:

  Object of class `"factor"` that lists the predictions of outcomes in the test set

`testScores`:

  Object of class `"ANY"` – this element will include matrices or vectors or arrays that include information that is typically related to the posterior probability of occupancy of the predicted class or of all classes. The actual contents of this slot can be determined by inspecting the converter element of the learnerSchema used to select the model.

`trainOutcomes`:

  Object of class `"factor"` that lists the actual outcomes in records on the training set

`trainPredictions`:

  Object of class `"factor"` that lists the predicted outcomes in the training set

`trainScores`:

  Object of class `"ANY"` see the description of `testScores` above; the same information is returned, but applicable to the training set records.

`trainInd`:

  Object of class `"numeric"` with of indices of data to be used for training.

`RObject`:

  Object of class `"ANY"` – when the `trainInd` parameter of the `MLearn` call is numeric, this slot holds the return value of the underlying R function that carried out the predictive modeling. For example, if `rpartI` was used as MLearn `method`, `Robject` holds an instance of the `rpart` S3 class, and `plot` and `text` methods can be applied to this. When the `trainInd` parameter of the `MLearn` call is an instance of `xvalSpec`, this slot holds a `list` of results of cross-validatory iterations. Each element of this list has two elements: `test.idx`, giving the numeric indices of the test cases for the associated cross-validation iteration, and `mlans`, which is the `classifierOutput` for the associated iteration. See the example for an illustration of 'digging out' the predicted probabilities associated with each cross-validation iteration executed through an xvalSpec specification.

`embeddedCV`:

  logical value that is TRUE if the procedure in use performs its own cross-validation

`fsHistory`:

  list of features selected through cross-validation process

`learnerSchema`:

  propagation of the learner schema object used in the call

`call`:

  Object of class `"call"` – records the call used to generate the classifierOutput RObject

### Methods

confuMat

  `signature(obj = "classifierOutput")`: Compute the confusion matrix for test records.

confuMatTrain

  `signature(obj = "classifierOutput")`: Compute the confusion matrix for training set. Typically yields optimistically biased information on misclassification rate.

RObject

  `signature(obj = "classifierOutput")`: The R object returned by the underlying classifier. This can then be passed on to specific methods for those objects, when they exist.

trainInd

  `signature(obj = "classifierOutput")`: Returns the indices of data used for training.

show

  `signature(object = "classifierOutput")`: A print method that provides a summary of the output of the classifier.

predictions

  `signature(object = "classifierOutput")`: Print the predicted classes for each sample/individual. The predictions for the training set are the training outcomes.

predictions

  `signature(object = "classifierOutput", t = "numeric")`: Print the predicted classes for each sample/individual that have a `testScore` greater or equal than `t`. The predictions for the training set are the training outcomes. Non-predicted cases and cases that matche multiple classes are returned as `NA`s.

predScore

  `signature(object = "classifierOutput")`: Returns the scores for predicted class for each sample/individual. The scores for the training set are set to 1.

predScores

  `signature(object = "classifierOutput")`: Returns the prediction scores for all classes for each sample/individual. The scores for the training set are set to 1 for the appropriate class, 0 otherwise.

testScores

  `signature(object = "classifierOutput")`: ...

testPredictions

  `signature(object = "classifierOutput")`: Print the predicted classes for each sample/individual in the test set.

testPredictions

  `signature(object = "classifierOutput", t = "numeric")`: Print the predicted classes for each sample/individual in the test set that have a `testScore` greater or equal than `t`. Non-predicted cases and cases that matche multiple classes are returned as `NA`s.

trainScores

  `signature(object = "classifierOutput")`: ...

trainPredictions

  `signature(object = "classifierOutput")`: Print the predicted classes for each sample/individual in the train set.

trainPredictions

  `signature(object = "classifierOutput", t = "numeric")`: Print the predicted classes for each sample/individual in the train set that have a `testScore` greater or equal than `t`. Non-predicted cases and cases that matche multiple classes are returned as `NA`s.

fsHistory

  `signature(object = "classifierOutput")`: ...

### Author(s)

V. Carey

### Examples

    showClass("classifierOutput")
    library(golubEsets)
    data(Golub_Train) # now cross-validate a neural net
    set.seed(1234)
    xv5 = xvalSpec("LOG", 5, balKfold.xvspec(5))
    m2 = MLearn(ALL.AML~., Golub_Train[1000:1050,], nnetI, xv5,
       size=5, decay=.01, maxit=1900 )
    testScores(RObject(m2)[[1]]$mlans)
    alls = lapply(RObject(m2), function(x) testScores(x$mlans))

[Package MLInterfaces version 1.60.1 Index]