extractPrediction

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Extract predictions and class probabilities from train objects

This function loops through a number of train objects and calculates the training and test data predictions and class probabilities

Keywords
manip
Usage
extractPrediction(object, testX = NULL, testY = NULL, unkX = NULL, 
   unkOnly = !is.null(unkX) & is.null(testX), verbose = FALSE)

extractProb(object, testX = NULL, testY = NULL, unkX = NULL, unkOnly = !is.null(unkX) & is.null(testX), verbose = FALSE)

Arguments
object
a list of objects of the class train. The objects must have been generated with fitBest = FALSE and returnData = TRUE.
testX
an optional set of data to predict
testY
an optional outcome corresponding to the data given in testX
unkX
another optional set of data to predict without known outcomes
unkOnly
a logical to bypass training and test set predictions. This is useful if speed is needed for unknown samples.
verbose
a logical for printing messages
Details

The optimal tuning values given in the tuneValue slot of the finalModel object are used to predict.

Value

  • For extractPrediction, a data frame with columns:
  • obsthe observed training and test data
  • predpredicted values
  • modelthe type of model used to predict
  • dataType"Training", "Test" or "Unknown" depending on what was specified
  • For extractProb, a data frame. There is a column for each class containing the probabilities. The remaining columns are the same as above (although the pred column is the predicted class)

Aliases
  • extractPrediction
  • extractProb
Examples
data(Satellite)
numSamples <- dim(Satellite)[1]
set.seed(716)

varIndex <- 1:numSamples

trainSamples <- sample(varIndex, 150)

varIndex <- (1:numSamples)[-trainSamples]
testSamples <- sample(varIndex, 100)

varIndex <- (1:numSamples)[-c(testSamples, trainSamples)]
unkSamples <- sample(varIndex, 50)

trainX <- Satellite[trainSamples, -37]
trainY <- Satellite[trainSamples, 37]

testX <- Satellite[testSamples, -37]
testY <- Satellite[testSamples, 37]

unkX <- Satellite[unkSamples, -37]

knnFit <- train(trainX, trainY, "knn")
rpartFit <- train(trainX, trainY, "rpart")

predTargets <- extractPrediction(list(knnFit, rpartFit), testX = testX, testY = testY, unkX = unkX)
Documentation reproduced from package caret, version 3.21, License: GPL-2

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