predict.EBRmodel: Predict Method for Ensemble of Binary Relevance

In utiml: Utilities for Multi-Label Learning

Description

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

Usage

## S3 method for class '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

# Predict SVM scores
model <- ebr(toyml)
pred <- predict(model, toyml)

# Predict SVM bipartitions running in 2 cores
pred <- predict(model, toyml, prob = FALSE, cores = 2)

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

utiml documentation built on May 31, 2021, 9:09 a.m.