EMNearestMeanClassifierSSLR: General Interface for EMNearestMeanClassifier model

In SSLR: Semi-Supervised Classification, Regression and Clustering Methods

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

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)

SSLR documentation built on July 22, 2021, 9:08 a.m.