## What are the Advantages and Disadvantages of Naïve Bayes Classifier?

**Advantages of Naive Bayes**

1. When assumption of independent predictors holds true, a Naive Bayes classifier performs better as compared to other models.

2. Naive Bayes requires a small amount of training data to estimate the test data. So, the training period is less.

3. Naive Bayes is also easy to implement.

**Disadvantages of Naive Bayes**

1. Main imitation of Naive Bayes is the **assumption
of independent predictors**. Naive Bayes implicitly assumes that all the
attributes are mutually independent. In real life, it is almost impossible that
we get a set of predictors which are completely independent.

2. If categorical variable has a category in test data set, which was not
observed in training data set, then model will assign a 0 (zero) probability
and will be unable to make a prediction. This is often known as **Zero
Frequency**.