What is the misclassification rates for each of the categories using of variable

Question

What is the misclassification rates for each of the categories using of variables? Beef Error the reduced set Rate 0.6667 Chicken Count Estimates for Category Priors 0.1429 Fish Frozen Tuna veggie Total The 0.1071 1.0000 0000 1.0000 1.0000 0 5000 0.0000 0.2679 total 0.3750 0.0536 0.3214 0.0357 0.0179 14 0.0179 67%, classification rate is 27%. The misclassification rate of the Beef is the is 50%, the Chicken is 14%, Ham and luna are 100% with 0% of Frozen and Veggie. How does using a prior equal (the default) compare to using prior proportional? Error Count Estimates for Category Beef Chicken Fish Frozen Ham Turkey veggie Total 0.5000 0.5238 0.3333 0.0556 1.0000 0 0000 Rate Priors 0.1250 0.1250 0.1250 0.1250 0 1250 0.1250 0 1250 0 1250 Compare to using prior proportional, the total misclassification rate is increasing to 33%. Why are the misclassification rates different?

Explanation / Answer

Prior equal is the default method of calculating the misclassification error. In this case the probability values is same for all the categories such that the sum of all probability values is equal to 1.

Prior proportional is the method where the probabilities are set proportional to the sample size because of which the misclassification error value is different from what we obtain in case of Prior equal probability values.

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