the prior and evidence values are easily determinedis the prior probability of class without knowing about the data this quantity can be obtained by simply calculating the fraction of all training data instances belonging to that particular class is the evidenceor the probability of features and this can be retrieved by calculating the fraction of all training data instances having that particular feature value the tricky part is the calculation of the likelihood it is the value describing how likely it is to see feature values and if we know that the class of the data instance is to estimate this we need bit more thinking being naive from the probability theorywe also know the following relationshipthis alonehoweverdoes not help muchsince we treat one difficult problem (estimating with another one (estimating howeverif we naively assume that and are independent from each othersimplifies to and we can write it as followsputting everything togetherwe get this quite manageable formulathe interesting thing is that although it is not theoretically correct to simply tweak our assumptions when we are in the mood to do soin this case it proves to work astonishingly well in real-world applications