predict.RDBRmodel: Predict Method for RDBR

In rivolli/utiml: Utilities for Multi-Label Learning

Description

This function predicts values based upon a model trained by rdbr. In general this method is a recursive version of predict.DBRmodel.

Usage

## S3 method for class 'RDBRmodel'
predict(
  object,
  newdata,
  estimative = NULL,
  max.iterations = 5,
  batch.mode = FALSE,
  probability = getOption("utiml.use.probs", TRUE),
  ...,
  cores = getOption("utiml.cores", 1),
  seed = getOption("utiml.seed", NA)
)

Arguments

object	Object of class 'RDBRmodel'.
newdata	An object containing the new input data. This must be a matrix, data.frame or a mldr object.
estimative	A matrix containing the bipartition result of other multi-label classification algorithm or an mlresult object with the predictions.
max.iterations	The maximum allowed iterations of the RDBR technique. (Default: 5)
batch.mode	Logical value to determine if use the batch re-estimation. If FALSE then use the stochastic re-estimation strategy. (Default: FALSE)
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))

Details

Two versions of the update strategy of the estimated labels are implemented. The batch re-estimates the labels only when a complete current label vector is available. The stochastic uses re-estimated labels as soon as they become available. This second does not support parallelize the prediction, however stabilizes earlier than batch mode.

Value

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

References

Rauber, T. W., Mello, L. H., Rocha, V. F., Luchi, D., & Varejao, F. M. (2014). Recursive Dependent Binary Relevance Model for Multi-label Classification. In Advances in Artificial Intelligence - IBERAMIA, 206-217.

See Also

Recursive Dependent Binary Relevance (RDBR)

Examples

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

# Passing a specif parameter for SVM predict algorithm
pred <- predict(model, toyml, na.action = na.fail)

# Use the batch mode and increase the max number of iteration to 10
pred <- predict(model, toyml, max.iterations = 10, batch.mode = TRUE)

# Using other classifier (EBR) to made the labels estimatives
estimative <- predict(ebr(toyml), toyml, probability = FALSE)
model <- rdbr(toyml, estimate.models = FALSE)
pred <- predict(model, toyml, estimative = estimative)

rivolli/utiml documentation built on June 1, 2021, 11:48 p.m.