predict.train

0th

Percentile

Extract predictions and class probabilities from train objects

These functions can be used for a single train object or to loop through a number of train objects to calculate the training and test data predictions and class probabilities.

Keywords
manip
Usage
## S3 method for class 'list':
predict(object, ...)

## S3 method for class 'train': predict(object, newdata = NULL, type = "raw", ...)

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

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

Details

These functions are wrappers for the specific prediction functions in each modeling package. In each case, the optimal tuning values given in the tuneValue slot of the finalModel object are used to predict.

To get simple predictions for a new data set, the predict function can be used. Limits can be imposed on the range of predictions. See trainControl for more information.

To get predictions for a series of models at once, a list of train objects can be passes to the predict function and a list of model predictions will be returned.

The two extraction functions can be used to get the predictions and observed outcomes at once for the training, test and/or unknown samples at once in a single data frame (instead of a list of just the predictions). These objects can then be passes to plotObsVsPred or plotClassProbs.

Value

  • For predict.train, a vector of predictions if type = "raw" or a data frame of class probabilities for type = "probs". In the latter case, there are columns for each class.

    For predict.list, a list results. Each element is produced by predict.train.

    For extractPrediction, a data frame with columns:

  • obsthe observed training and test data
  • predpredicted values
  • modelthe type of model used to predict
  • objectthe names of the objects within models. If models is an un-named list, the values of object will be "Object1", "Object2" and so on
  • 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)

References

Kuhn (2008), ``Building Predictive Models in R Using the caret'' (http://www.jstatsoft.org/v28/i05/)

See Also

plotObsVsPred, plotClassProbs, trainControl

Aliases
Examples
library(mlbench)
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")

predict(knnFit)
predict(knnFit, newdata = testX)
predict(knnFit, type = "prob")

bothModels <- list(
                   knn = knnFit,
                   tree = rpartFit)

predict(bothModels)

predTargets <- extractPrediction(
                                 bothModels,
                                 testX = testX,
                                 testY = testY,
                                 unkX = unkX)
Documentation reproduced from package caret, version 5.07-001, License: GPL-2

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