library(e1071) x1<-c(0,0.8,0.4,0.3,0.1,0.7,0.5,0.8,0.8,0.8) x2<-c(0.1,0.9,0.5,0.7,0.4,0.3,0.2,0.6,0,0.3) x<-cbind(x1,x2) y<-as.factor(c(-1,-1,-1,-1,-1,1,1,1,1,1))
plot(x,pch=19,xlim=c(0,1),ylim=c(0,1),col=2*as.numeric(y),cex=2, xlab=expression(x[1]),ylab=expression(x[2]))
Use svm() with kernel="linear" and cost=100000 to fit the toy 2D data below. Provide a plot of the resulting class rule.
fit<-svm(x=x,y=y,kernel="linear",cost=100000) 1-sum(y==predict(fit,x))/length(y)
To visualize classification, generate numbers, run them thru model, and color the output based on the label (class) prediction.
big_x<-matrix(runif(200000),ncol=2,byrow=T) plot(big_x,col=rgb(0.5,0.5,0.2+0.6*as.numeric(predict(fit,big_x)==1)),pch=19) points(x,pch=19,xlim=c(0,1),ylim=c(0,1),col=2*as.numeric(y),cex=2, xlab=expression(x[1]),ylab=expression(x[2])) abline(-0.05,1,lwd=6)
```r x<-function(k1,k2){
}
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