predict.DBRmodel {utiml} | R Documentation |
Predict Method for DBR
Description
This function predicts values based upon a model trained by dbr
.
In general this method is a restricted version of
predict.BRPmodel
using the 'NU' strategy.
Usage
## S3 method for class 'DBRmodel'
predict(
object,
newdata,
estimative = NULL,
probability = getOption("utiml.use.probs", TRUE),
...,
cores = getOption("utiml.cores", 1),
seed = getOption("utiml.seed", NA)
)
Arguments
object |
Object of class ' |
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. |
probability |
Logical indicating whether class probabilities should be
returned. (Default: |
... |
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:
|
seed |
An optional integer used to set the seed. This is useful when
the method is run in parallel. (Default: |
Details
As new feature is possible to use other multi-label classifier to predict the estimate values of each label. To this use the prediction argument to inform a result of other multi-label algorithm.
Value
An object of type mlresult, based on the parameter probability.
References
Montanes, E., Senge, R., Barranquero, J., Ramon Quevedo, J., Jose Del Coz, J., & Hullermeier, E. (2014). Dependent binary relevance models for multi-label classification. Pattern Recognition, 47(3), 1494-1508.
See Also
Dependent Binary Relevance (DBR)
Examples
# Predict SVM scores
model <- dbr(toyml)
pred <- predict(model, toyml)
# Passing a specif parameter for SVM predict algorithm
pred <- predict(model, toyml, na.action = na.fail)
# Using other classifier (EBR) to made the labels estimatives
estimative <- predict(ebr(toyml), toyml)
model <- dbr(toyml, estimate.models = FALSE)
pred <- predict(model, toyml, estimative = estimative)