USMLeastSquaresClassifierSSLR: General Interface for USMLeastSquaresClassifier (Updated...

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

Description

model from RSSL package This methods uses the closed form solution of the supervised least squares problem, except that the second moment matrix (X'X) is exchanged with a second moment matrix that is estimated based on all data. See for instance Shaffer1991, where in this implementation we use all data to estimate E(X'X), instead of just the labeled data. This method seems to work best when the data is first centered x_center=TRUE and the outputs are scaled using y_scale=TRUE.

Usage

USMLeastSquaresClassifierSSLR(
  lambda = 0,
  intercept = TRUE,
  x_center = FALSE,
  scale = FALSE,
  y_scale = FALSE,
  ...,
  use_Xu_for_scaling = TRUE
)

Arguments

lambda	numeric; L2 regularization parameter
intercept	logical; Whether an intercept should be included
x_center	logical; Should the features be centered?
scale	logical; Should the features be normalized? (default: FALSE)
y_scale	logical; whether the target vector should be centered
...	Not used
use_Xu_for_scaling	logical; whether the unlabeled objects should be used to determine the mean and scaling for the normalization

References

Shaffer, J.P., 1991. The Gauss-Markov Theorem and Random Regressors. The American Statistician, 45(4), pp.269-273.

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 <- USMLeastSquaresClassifierSSLR() %>% 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.