What’s “Naive” about Naive Bayes Machine Learning Algorithm?

Standard

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)

IncomeCreditScoreApproved
HighHighYes
HighMediumYes
LowHighYes
LowLowNO

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!

Note from Paras: To support this blog and free content here, I partnered with Datacamp for affiliate marketing revenue. I personally use this platform (see my profile here) and highly recommend it. I wouldn't be endorsing them if it wasn't 10/10! Click on the link below to start your journey towards becoming the BEST data professional! datacampParas's datacamp referral link

0 thoughts on “What’s “Naive” about Naive Bayes Machine Learning Algorithm?

What do you think? Leave a comment below.