compute_multilabel_predictions: Compute the multi-label ensemble predictions based on some... In utiml: Utilities for Multi-Label Learning

Description

Compute the multi-label ensemble predictions based on some vote schema

Usage

 ```1 2``` ```compute_multilabel_predictions(predictions, vote.schema = "maj", probability = getOption("utiml.use.probs", TRUE)) ```

Arguments

 `predictions` A list of multi-label predictions (mlresult). `vote.schema` Define the way that ensemble must compute the predictions. The default valid options are: 'avg'Compute the mean of probabilities and the bipartitions 'maj'Compute the majority of votes 'max'Compute the higher probability for each instance/label 'min'Compute the lower probability for each instance/label . (Default: 'maj') `probability` A logical value. If `TRUE` the predicted values are the score between 0 and 1, otherwise the values are bipartition 0 or 1.

Value

A mlresult with computed predictions.

Note

You can create your own vote schema, just create a method that receive two matrix (bipartitions and probabilities) and return a list with the final bipartitions and probabilities.

Remember that this method will compute the ensemble votes for each label. Thus the bipartition and probability matrix passed as argument for this method is related with the bipartitions and probabilities for a single label.

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21``` ```## Not run: model <- br(toyml, "KNN") predictions <- list( predict(model, toyml[1:10], k=1), predict(model, toyml[1:10], k=3), predict(model, toyml[1:10], k=5) ) result <- compute_multilabel_predictions(predictions, "maj") ## Random choice random_choice <- function (bipartition, probability) { cols <- sample(seq(ncol(bipartition)), nrow(bipartition), replace = TRUE) list( bipartition = bipartition[cbind(seq(nrow(bipartition)), cols)], probability = probability[cbind(seq(nrow(probability)), cols)] ) } result <- compute_multilabel_predictions(predictions, "random_choice") ## End(Not run) ```

utiml documentation built on April 20, 2018, 1:04 a.m.