Let's use the example of classifying the gender of a person. You give an input observation, our Naive Bayes Classifier should output a category.

**Features**:

In Naive Bayes Classifier, or any general ML Classification Algorithm, the data points we choose to define our input.

**For example:**

We can't possibly input all data points about a person; instead, we pick a few features to define a person. In a __Naive Bayes Classifier__, the key assumption we make is that these features are independent of a person's height doesn't affect weight doesn't affect foot size. This assumption may or not be true, but for a Naive Bayes, we assume that it is true.

**Parameters**: Parameters in Naive Bayes are the estimates of the true distribution of classified points.

**For example: **

We can say that roughly 50% of people are male, and the distribution of male height is a Gaussian distribution with mean 5' 7" and standard deviation 3". The parameters would be the 50% estimate, the 5' 7" mean estimate, and the 3" standard deviation estimate.

More details could be found out while studying Machine Learning Tutorial and Deep Learning Tutorial.

I hope that was helpful!