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.
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 ) ## S3 method for class 'train' predict(object, newdata = NULL, type = "raw", na.action = na.omit, ...)
models |
a list of objects of the class |
testX |
an optional set of data to predict |
testY |
an optional outcome corresponding to the data given in
|
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 |
object |
For |
newdata |
an optional set of data to predict on. If |
type |
either "raw" or "prob", for the number/class predictions or class probabilities, respectively. Class probabilities are not available for all classification models |
na.action |
the method for handling missing data |
... |
only used for |
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.
For predict.train, a vector of predictions if type = "raw" or
a data frame of class probabilities for type = "prob". 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:
obs |
the observed training and test data |
pred |
predicted values |
model |
the type of model used to predict |
object |
the names of
the objects within |
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
Kuhn (2008), “Building Predictive Models in R Using the caret” (http://www.jstatsoft.org/article/view/v028i05/v28i05.pdf)
## Not run:
knnFit <- train(Species ~ ., data = iris, method = "knn",
trControl = trainControl(method = "cv"))
rdaFit <- train(Species ~ ., data = iris, method = "rda",
trControl = trainControl(method = "cv"))
predict(knnFit)
predict(knnFit, type = "prob")
bothModels <- list(knn = knnFit,
tree = rdaFit)
predict(bothModels)
extractPrediction(bothModels, testX = iris[1:10, -5])
extractProb(bothModels, testX = iris[1:10, -5])
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.