SSLR (version 0.9.3.3)

EMNearestMeanClassifierSSLR: General Interface for EMNearestMeanClassifier model

Description

model from RSSL package Semi-Supervised Nearest Mean Classifier using Expectation Maximization

Expectation Maximization applied to the nearest mean classifier assuming Gaussian classes with a spherical covariance matrix.

Starting from the supervised solution, uses the Expectation Maximization algorithm (see Dempster et al. (1977)) to iteratively update the means and shared covariance of the classes (Maximization step) and updates the responsibilities for the unlabeled objects (Expectation step).

Usage

EMNearestMeanClassifierSSLR(method = "EM", scale = FALSE, eps = 1e-04)

Arguments

method

character; Currently only "EM"

scale

Should the features be normalized? (default: FALSE)

eps

Stopping criterion for the maximinimization

References

Dempster, A., Laird, N. & Rubin, D., 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B, 39(1), pp.1-38.

Examples

# NOT RUN {
library(tidyverse)
library(tidymodels)
library(caret)
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 <- EMNearestMeanClassifierSSLR() %>% fit(Class ~ ., data = train)

#Accesing model from RSSL
model <- m$model

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