Different accuracy for different rng values 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)
The training errors in this dataset has a huge difference (99% vs 57%). So, maybe the one with the rng(1) split has overfitted your dataset.


  So there is a huge change in accuracy as visible. What does this mean?
  Am I training it wrong?


The huge change might be due to overfitting. (Also, judge the model through validation curves, and then fit a model which balances the bias-variance plot.)