Python Shuffling in classification problems

[EngWiPy]

Hello all,

This is not particular to Python, it is more conceptual related to machine learning algorithms, specifically the nearest neighbor classifiers. So, I have this dataset with m examples, each with n features and one target feature. The dataset is originally ordered such that similar target features are placed contiguously: AAA..A, BBB...B, ... etc. I did 10-fold cross validation on the original dataset and got an accuracy of 0.86. Then I shuffled the dataset and did the same cross validation procedure but got an accuracy of 0.32. My question is: is this expected and why since I compute the average accuracy? Is shuffling the data legitimate in the first place?

Thanks in advance

 

[EngWiPy]

If this question doesn't fit, could anyone recommend me some good and active forums where I can pose my questions related to machine learning and data analytics? Thanks

 

[jedishrfu]

What are you trying to do in a more concrete way?

If you were trying to identify cards in a deck of cards and say organized them by card value: aces, deuces, ... jacks, queens, kings and trained your ANN to recognize them then shuffling would be okay in my opinion because the ANN is being trained to recognize cards and not sequences of cards.

 

[EngWiPy]

I was trying to identify three wheat classes: Canadian, Kama, and Rosa based on a set of measurements of the kernel. The original dataset was organized based on the class like: C, C, ..., C, K, K, ..., K, R, R, ..., R. At first I did the cross validation using the original dataset. But then I shuffled it and did the cross validation again. After some inspection I discovered I had two problems:

1- The indentation of my code wasn't correct, and Python is sensitive to indentation to identify blocks of codes like within a for loop.
2- I didn't normalize the measurements to a common scale, e.g. using z-score normalization.

After correcting the above two mistakes, my code now gives me a 10-fold cross validation performance of 0.86 without shuffling, and 0.96 with random shuffling and 0.94 when I select the test block from the original data for each fold as

for i in range(fold):
...
    testing = data[i::fold]  ...