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My method of evaluating a model is the following :

  1. Split the training data set and do cross validation to obtain an accuracy of my model on my cross validation data set.
  2. Use the parameters that gave me the best accuracy and use predict() on my test data set ( hold-out data set )
  3. Run a little 'for' loop to check how many labels i missclassified ( Let's assume i'm doing classification ) on my test dataset for which i hid the real labels.
  4. I'd look at the percentages of 'accuracy' given by each algorithm and pick the best accuracy.

My question :

What can be done to improve my method of error-analysis and model evaluation using Python? Code snippets and their purposes would be helpful.

Thanks!

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