Naive Bayes – pros and cons

In this section, we present the advantages and disadvantages in selecting the Naive Bayes algorithm for classification problems.

These are the pros:

• Training time: The Naive Bayes algorithm only requires one pass on the entire dataset to calculate the posterior probabilities for each value of the feature in the dataset. So, when we are dealing with large datasets or low-budget hardware, the Naive Bayes algorithm is a feasible choice for most data scientists.

• Prediction time: Since all the probabilities are pre-computed in the Naive Bayes algorithm, the prediction time of this algorithm is very efficient.

• Transparency: Since the predictions of Naive Bayes algorithms are based on the posterior probability of each conditional feature, it is easy to understand which features are influencing the predictions. This helps users to understand the...