Supervised Classification, Logistic and Multinomial

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We will start, in our Data Science course,  to discuss classification techniques (in the context of supervised models). Consider the following case, with 10 points, and two classes (red and blue) > clr1 <- c(rgb(1,0,0,1),rgb(0,0,1,1)) > clr2 <- c(rgb(1,0,0,.2),rgb(0,0,1,.2)) > x <- c(.4,.55,.65,.9,.1,.35,.5,.15,.2,.85) > y <- c(.85,.95,.8,.87,.5,.55,.5,.2,.1,.3) > z <- c(1,1,1,1,1,0,0,1,0,0) > df <- data.frame(x,y,z) > plot(x,y,pch=19,cex=2,col=clr1[z+1]) To get a prediction, i.e. a partition of the space in two parts, consider some logistic regression > reg=glm(z~x+y,data=df,family=binomial) > summary(reg)   Call: glm(formula = z ~ … Continue reading Supervised Classification, Logistic and Multinomial →

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