Multi-Label Prediction Using Gibbs Sampling (and Classifier Chains)

An implementation of classifier chains (CC's) for multi-label prediction. Users can employ an external package (e.g. 'randomForest', 'C50'), or supply their own. The package can train a single set of CC's or train an ensemble of CC's -- in parallel if running in a multi-core environment. New observations are classified using a Gibbs sampler since each unobserved label is conditioned on the others. The package includes methods for evaluating the predictions for accuracy and aggregating across iterations and models to produce binary or probabilistic classifications.


Multi-Label Prediction Using Gibbs Sampling (and Classifier Chains)

An implementation of classifier chains (CCs) for multi-label prediction. Users can employ an external package (e.g. 'randomForest', 'C50'), or supply their own. The package can train a single set of CCs or train an ensemble of CCs -- in parallel if running in a multi-core environment. New observations are classified using a Gibbs sampler since each unobserved label is conditioned on the others. The package includes methods for evaluating the predictions for accuracy and aggregating across iterations and models to produce binary or probabilistic classifications.

Installation

if ( !('devtools' %in% installed.packages()) ) install.packages("devtools")
 
devtools::install_github("bearloga/MLPUGS") # or...
devtools::install_github("bearloga/MLPUGS", build_vignettes = TRUE)

Basic Usage

fit <- ecc(x, y)
preds <- predict(fit, x_new)
y_pred <- summary(preds)

For a detailed tutorial, please see browseVignettes(package="MLPUGS").

External Classifiers

Currently, there is no built-in classifier in version 0.1.1, but users can supply their own or use an existing package. For example:

# Random Forest:
foo_train <- function(x, y) randomForest::randomForest(x, y)
foo_predict <- function(x, newdata) randomForest:::predict.randomForest(x, newdata, type = "prob")
 
# C5.0:
foo_train <- function(x, y) C50::C5.0(x, y)
foo_predict <- function(x, newdata) C50::predict.C5.0(x, newdata, type = "prob")
 
fit <- ecc(x, y, .f = foo_train)
pugs <- predict(fit, x_new, .f = foo_predict)
y_pred <- summary(pugs, type = "prob")
 
y_pred <- ecc(x, y, .f = foo_train) %>%
          predict(x_new, .f = foo_predict) %>%
          summary(type = "prob")

Code of Conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

News

Reference manual

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install.packages("MLPUGS")

0.2.0 by Mikhail Popov, 3 years ago


https://github.com/bearloga/MLPUGS


Report a bug at https://github.com/bearloga/MLPUGS/issues


Browse source code at https://github.com/cran/MLPUGS


Authors: Mikhail Popov [aut, cre] (@bearloga on Twitter)


Documentation:   PDF Manual  


MIT + file LICENSE license


Suggests knitr, progress, C50, randomForest


See at CRAN