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MLPUGS

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.

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Install

install.packages('MLPUGS')

Monthly Downloads

110

Version

0.2.0

License

MIT + file LICENSE

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Maintainer

Last Published

July 6th, 2016

Functions in MLPUGS (0.2.0)

ecc

Fit an Ensemble of Classifier Chains (ECC)
MLPUGS-package

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

FiveThirtyEight's Movie Scores
predict.ECC

Classify new samples using an Ensemble of Classifier Chains
summary.PUGS

Gather samples of predictions
validate_pugs

Assess multi-label prediction accuracy