EntropyRegularizedLogisticRegressionSSLR {SSLR}

General Interface for EntropyRegularizedLogisticRegression model

Description

model from RSSL package R Implementation of entropy regularized logistic regression implementation as proposed by Grandvalet & Bengio (2005). An extra term is added to the objective function of logistic regression that penalizes the entropy of the posterior measured on the unlabeled examples.

Usage

EntropyRegularizedLogisticRegressionSSLR(
  lambda = 0,
  lambda_entropy = 1,
  intercept = TRUE,
  init = NA,
  scale = FALSE,
  x_center = FALSE
)

Arguments

lambda

l2 Regularization

lambda_entropy

Weight of the labeled observations compared to the unlabeled observations

intercept

logical; Whether an intercept should be included

init

Initial parameters for the gradient descent

scale

logical; Should the features be normalized? (default: FALSE)

x_center

logical; Should the features be centered?

References

Grandvalet, Y. & Bengio, Y., 2005. Semi-supervised learning by entropy minimization. In L. K. Saul, Y. Weiss, & L. Bottou, eds. Advances in Neural Information Processing Systems 17. Cambridge, MA: MIT Press, pp. 529-536.

Examples

library(tidyverse)
library(caret)
library(tidymodels)
library(SSLR)

data(breast)

set.seed(1)
train.index <- createDataPartition(breast$Class, p = .7, list = FALSE)
train <- breast[ train.index,]
test  <- breast[-train.index,]

cls <- which(colnames(breast) == "Class")

#% LABELED
labeled.index <- createDataPartition(breast$Class, p = .2, list = FALSE)
train[-labeled.index,cls] <- NA


m <- EntropyRegularizedLogisticRegressionSSLR() %>% fit(Class ~ ., data = train)


#Accuracy
predict(m,test) %>%
  bind_cols(test) %>%
  metrics(truth = "Class", estimate = .pred_class)