In this post, I’ll post what why does the “Naive Bayes machine learning” algo have the word Naive in it?
So here is the short answer:
It “assumes” that the features are independent. (In other words: There’s no relation between the features that are used while building the model)
Let’s go a little deeper:
First up, few basic pointers.
> It’s a machine learning algorithm used for classification
> It’s based on Bayesian Statistics.
> you can read about it here: http://en.wikipedia.org/wiki/Naive_Bayes_classifier
Now, what do you mean when you mean that it is Naive because it assumes that features are independent?
Let’s take an example:
Suppose, you are building a “credit card approval” model based on Income and CreditScore
(SideNote: For those who do not know what is credit score, here you go: http://en.wikipedia.org/wiki/Credit_score_in_the_United_States)
And you have the following columns in the training data (Note: In machine learning, think of this columns as features)
| Income | CreditScore | Approved |
| High | High | Yes |
| High | Medium | Yes |
| Low | High | Yes |
| Low | Low | NO |
Here the features are Income & CreditScore and the target of the classification model is Approved.
In real world, there’s some relation between “income” and “creditscore”. Agree? Great! But Naive Bayes doesn’t think so. Let me reiterate the point of this blog post and see if it makes more sense now: it assumes that the features are “independent” and that’s why it is Naive!
I hope this helps. your comments are very welcome!