Question

Why is Naive Bayes called 'naive'?

A) Because it assumes independence between features
B) Because it is a simple and straightforward algorithm
C) Because it is not based on any statistical principles
D) Because it always produces accurate predictions

Answer 1

Naive Bayes is considered 'naive' because it assumes that all features in a dataset are independent of each other given the class, despite the reality where features can be dependent.

Step-by-step explanation:

Naive Bayes is called 'naive' because of option A) It assumes independence between features.

The algorithm operates under the assumption that the features (or variables) used to predict the probability of a certain outcome are independent of each other. This is often termed the "naive" assumption since in many real-world scenarios, features might exhibit some level of interdependence or correlation. Despite this oversimplified assumption, Naive Bayes has been found to perform well in various applications, especially in text classification and spam filtering.

The term "naive" in Naive Bayes doesn't suggest that it's simplistic in a negative sense but rather refers to the simplifying assumption it makes about the independence of features, which might not always hold true in practical situations.