extractPrediction {caret} | R Documentation |
This function loops through a number of train objects and calculates the training and test data predictions and class probabilities
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)
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 |
The optimal tuning values given in the tuneValue
slot of the finalModel
object are used to predict.
For extractPrediction
, a data frame with columns:
obs |
the observed training and test data |
pred |
predicted values |
model |
the 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)
Max Kuhn
## Not run: 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) ## End(Not run)