"Naive"?

We are in a probabilistic world!

Bayes Theorem?

Time to combine the concepts of Naive Independent assumption and Bayes Theorem into one

According to Bayes' Theorem -

According to independent assumption of predictors -

After combination of both the concept -

How does the Naive Bayes Classifier perform so well with a wrong assumption?

- Because it employs a very simple hypothesis function, it exhibits a very high bias but relatively low variance, that prevents it from over-fitting to its training data.



- It’s not sensitive to irrelevant features.





When to use Naive Bayes Classifier for classification?



- It performs magnificently in multi-class prediction.



- It also works so well in text classification.



- Spam Filtering



- Sentiment Analysis



- Recommendation System - Naive Bayes Classifier along with Collaborative Filtering make a great Recommendation System.



- You may prefer it when you require less model training time. It's fast. So fast.



- Unlike Neural Network and other classifiers, it is not black box. It is so easy to understand and interpret.





Since Naive Bayes Classification is based on probabilistic approach, the concept and its application is never gonna fade away.



Don't you agree with me? Do you? Much appreciated if let me know your thoughts in the comments below. Please do share the post with your friends as well!







If you logically segment "Naive Bayes Classification" into words then you will end up with two terms that makes more sense independently:- Naive- Bayes TheoremSo, if we go through these two terms separately and then combine the concepts into one, we can understand Naive Bayes Classification so easily, isn't it?- Inexperienced- Just like a naive (inexperienced) little child who makes some assumptions that are not completely true.So, what is this assumption all about?- "Independent assumption of predictor"- i.e. presence of a particular feature in a class is unrelated to the presence of any other features.How does Naive Bayes' Classifier differ from other classifiers?- A big difference!- Unlike many other classifiers which assume or find for some correlation among features, Naive Bayes classifier completely abandon the concept of correlation.- Seems illogical, doesn't it? - The reason we call it "Naive"Let's give this "independent assumption" a mathematical shape: If two events A and B are independent thene.g.Assumption:Independent assumption:Let's say, you see a guy and you are 80% sure that his name is Vito Corleone AND 90% sure that he is from Sicily. So, with our "independent assumption", now calculate what are the chances that he is The Godfather?orThere are 72% chances that he is The Godfather! Respect!- Probability of an event given that another even has already been occurred.- Example: Probability of occurrence ofgiven that another eventhas already been occurred or in mathematical language findAlready Given Information- Lets forecast rain for tomorrow- Predictors / Independent variables:- Probability that rain will happenif- Humidity- Atmospheric PressureSimilarly, we can calculate for different options e.g.and so on.- The option with the maximum probability would be a right classifier.- The independent assumption of predictor limits it significantly, and as a result this makes it very less prone to get stuck in Local Minima.- Since predictors are independent to each other, the interactions among them are not modeled, so relatively it needs less training data. As a result, it performs well even with small data-sets as well as with missing data too.