utiml (version 0.1.4)

predict.EBRmodel: Predict Method for Ensemble of Binary Relevance

Description

This method predicts values based upon a model trained by ebr.

Usage

# S3 method for EBRmodel
predict(object, newdata, vote.schema = "maj",
  probability = getOption("utiml.use.probs", TRUE), ...,
  cores = getOption("utiml.cores", 1), seed = getOption("utiml.seed", NA))

Arguments

object

Object of class 'EBRmodel'.

newdata

An object containing the new input data. This must be a matrix, data.frame or a mldr object.

vote.schema

Define the way that ensemble must compute the predictions. The default valid options are: c("avg", "maj", "max", "min"). If NULL then all predictions are returned. (Default: 'maj')

probability

Logical indicating whether class probabilities should be returned. (Default: getOption("utiml.use.probs", TRUE))

...

Others arguments passed to the base algorithm prediction for all subproblems.

cores

The number of cores to parallelize the training. Values higher than 1 require the parallel package. (Default: options("utiml.cores", 1))

seed

An optional integer used to set the seed. This is useful when the method is run in parallel. (Default: options("utiml.seed", NA))

Value

An object of type mlresult, based on the parameter probability.

See Also

Ensemble of Binary Relevance (EBR) Compute Multi-label Predictions

Examples

# NOT RUN {
# Predict SVM scores
model <- ebr(toyml)
pred <- predict(model, toyml)

# Predict SVM bipartitions running in 6 cores
pred <- predict(model, toyml, prob = FALSE, cores = 6)

# Return the classes with the highest score
pred <- predict(model, toyml, vote = 'max')
# }