python - How to use the a k-fold cross validation in scikit with naive bayes classifier and NLTK -

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i have small corpus , want calculate accuracy of naive bayes classifier using 10-fold cross validation, how can it.

your options either set or use nltk-trainer since nltk doesn't directly support cross-validation machine learning algorithms.

i'd recommend using module if want write own code following.

supposing want 10-fold, have partition training set 10 subsets, train on 9/10, test on remaining 1/10, , each combination of subsets (10).

assuming training set in list named training, simple way accomplish be,

num_folds = 10 subset_size = len(training)/num_folds in range(num_folds):     testing_this_round = training[i*subset_size:][:subset_size]     training_this_round = training[:i*subset_size] + training[(i+1)*subset_size:]     # train using training_this_round     # evaluate against testing_this_round     # save accuracy  # find mean accuracy on rounds

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