rvi008/svm

TP SVM

Question 1

We load the Iris Dataset and train a simple Linear Kernel SVM. The fiting score is 66%. We get a cross validated score of 72% +/-16% accuracy.

Question 2

We now train a polynomial kernel SVM and get a fiting score of 72% (better) but the cross validated score is 72% +/- 16%. No improvement

Question 3

See Notebook

Question 4

The classification error 1 or 0 has 2 shelves, so we can't minimise this constant by chunk and discountinuous function. We use the Hinge function which is majorating the error function (see plot in notebook). It is a convex function so we can minimise it with a gradient descent.

Question 5

We can see in the plot that as C increases, the prediction error decreases until it's stable. The coefficient tunes the number of classification errors we accept when larger we accept less errors.*

Question 6

As expected when we add columns of noise the performances decreases but the rate is not so strong

Question 7

The features are standardized because the SVM algorithm lays on distances calculations. So we need to reduce the importance of the big values.

Question 8

The plot clearly shows that the performances are significantly lower than those of the linear kernel SVM.