figure classification accuracy is measure representing what percentage of observations was correctly classified on the test data the accuracy on the test data is estimated to be greater than %as seen in figure classification accuracy is the number of correctly classified observations as percentage of the total number of observations be advisedthoughthat this can be different in your case if you used different data nltk classify accuracy(classifiertestthat' good number we can now lean back and relaxrightnonot really let' test it again on the observations holdout sample and this time create confusion matrix classified_data classifier classify_many(prepared_holdout_datacm nltk confusionmatrix(holdout_data_labelsclassified_dataprint cm the confusion matrix in figure shows us the is probably over the top because we have ( misclassified cases againthis can be different with your data if you filled the sqlite file yourself figure naive bayes model confusion matrix shows ( observations out of were misclassified twenty-eight misclassifications means we have an accuracy on the holdout sample this needs to be compared to randomly assigning new post to either the "datascienceor "gameofthronesgroup if we' randomly assigned themwe could expect an