Prediction on Test Data using Unsupervised VarPro

Obtain predicted values on test data for unsupervised forests.

# S3 method for class 'uvarpro'
predict(object, newdata, ...)

Arguments

object

Unsupervised VarPro object from a previous call to uvarpro. Only applies if method = "auto" was used.

newdata

Optional test data. If not provided, the training data is used.

...

Additional arguments passed to internal methods.

Details

Applies to unsupervised VarPro objects built using the autoencoder (method = "auto"). The object contains a multivariate random forest used to generate predictions for the test data.

Returns a matrix of predicted values, where each column corresponds to a feature (with one-hot encoding applied). The result includes the following attributes:

1. mse: Standardized mean squared error averaged across features.

2. mse.all: Standardized mean squared error for each individual feature.

Author

Min Lu and Hemant Ishwaran

See also

uvarpro

Examples

# \donttest{
## ------------------------------------------------------------
##
## boston housing
## obtain predicted values for the training data
##
## ------------------------------------------------------------

## unsupervised varpro on boston housing
data(BostonHousing, package = "mlbench")
o <- uvarpro(data=BostonHousing)

## predicted values for the training features
print(head(predict(o)))

## ------------------------------------------------------------
##
## mtcars
## obtain predicted values for test data
## also illustrates hot-encoding working on test data
##
## ------------------------------------------------------------

## mtcars with some factors
d <- data.frame(mpg=mtcars$mpg,lapply(mtcars[, c("cyl", "vs", "carb")], as.factor))

## training 
o <- uvarpro(d[1:20,])

## predicted values on test data
print(predict(o, d[-(1:20),]))

## predicted values on bad test data with strange factor values 
dbad <- d[-(1:20),]
dbad$carb <- as.character(dbad$carb)
dbad$carb <-  sample(LETTERS, size = nrow(dbad))
print(predict(o, dbad))

# }