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.
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.
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.
show
signature(object = "classifierOutput"): A print method that provides a summary of the output of the classifier.
testScores
signature(object = "classifierOutput"): ...
testPredictions
signature(object = "classifierOutput"): Print the predicted classes for each sample/individual in the test set.
trainPredictions
signature(object = "classifierOutput"): Print the predicted classes for each sample/individual in the training set.
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 1.14.1 Index]