ChooseAlgorithm: Returns which algorithm among Multiclass SVM, KNN and...

In gargjhanvi/SSLLabelGuide: Classify what data points should be labelled(for training) based on budget constraints and very small or no labelled data for semi supervised learning, predict the labels of unlabelled data and determine the confidence of labelling them

Description

Returns which algorithm among Multiclass SVM, KNN and Multiclass logistic performs better on the labelled data set using cross validation technique

Usage

ChooseAlgorithm(Xtilde, Y, K = NULL)

Arguments

Xtilde	n * p centered and scaled data points
Y	A numeric n vector of labels of Xtilde
K	Number of classes, ( If K is NULL, it calculates K as maximum entry in Y)

Value

A string among c("KNN", "Logistic", "SVM") which tells us which algorithm performs better on the labelled data set

Examples

X <- rbind(matrix(rnorm(10,0,1),5,2),matrix(rnorm(10,1,2),5,2), matrix(rnorm(10,4,3),5,2))
fold_ids <- sample(1:15)
X <- X[fold_ids, ]
Xtilde <- standardizeX(X)$tilde
Y <- c(1,1,1,1,1,2,2,2,2,2,3,3,3,3,3)
Y <- Y[fold_ids]
out <- ChooseAlgorithm(Xtilde,Y,3)
#"KNN"

gargjhanvi/SSLLabelGuide documentation built on Dec. 20, 2021, 9:48 a.m.