2

While tuning the SVM classification model in Matlab, I came across the rng function in matlab in which seed (stabilizes the random shuffling of the data in the algorithm) is changed. When the function called is rng(1) then I am getting one accuracy value (99%). When it is changed to rng(2) then I am getting another value (57%). So there is a huge change in accuracy as visible. What does this mean? Am I training it wrong?

The train and test set correct rate (in %) that I am getting with different runs without changing rng are(train,test)

(96, 82.8)

(94.6, 95.3)

(96, 85.9)

(96, 90)

(95, 95)

1What are the testing errors of both? Nothing can be said by just looking at the training errors. Maybe

`rng(1)`

is better than`rng(2)`

or maybe it is overfitting. – Dawny33 – 2016-08-09T05:48:31.843@Dawny33 So does this mean that there is overfitting? A good model should have the same accuracy for any rng right? – girl101 – 2016-08-09T05:52:22.610