Frequently Asked Interview Questions on Naive Bayes Classifier

 This article was published as a part of the Data Science Blogathon.

Introduction

We, as data science and machine learning enthusiasts, have learned about various algorithms like Logistic Regression, Linear Regression, Decision Trees, Naive Bayes, etc. But at the same time, are we preparing for the interviews? As we know, the end goal is to land our dream job for the companies we are aiming for. Henceforth, knowing how the questions are turned and twisted by the interviewer is very much important to answer in the most efficient reason; I’m starting with the series of the Top 10 most frequently asked interview questions on various machine learning algorithms.

In this article, we will be covering the top 10 interview questions on the Naive Bayes classifier, but we are not gonna jump straight over those tricky questions; instead, let’s first have some high-level understanding of this algorithm so that one will be able to understand the concept behind it.

Naive Bayes classifier

Naive Bayes is considered to be the top choice while dealing with classification problems, and it has it’s rooted in the concept of probabilities. Specifically, this algorithm is the by-product of the Bayes Theorem. But you must be thinking that if it is based on Bayes theorem, why is this Naive term in the prefix position as “Naive” means “Dumb”? So is this algorithm dumb or useful?

The answer is simple and pretty straightforward; this algorithm is not at all Naive but, at times, quite useful and simple when compared to other complex algorithms. The reason it is known to be the naive Bayes is because of its general assumptions, which takes us to our very first interview question:

1. What is the basic assumption in the case of the Naive Bayes classifier?

If one wants to give the short answer, then they can simply say – “Features are independent.” But this will not be sufficient; hence we need to explain the answer briefly: In Naive Bayes, it assumes beforehand that all the features are independent of each other, and it treats all of them separately, which gives each feature an equal contribution to the final result. This assumption is known as the I.I.D assumption.

2. What are the possible advantages of choosing the Naive Bayes classifier?

1. As it works independently with each feature, we can use it with large datasets for making generalized models.
2. It has very much less sensitive to other features, i.e.; it is not much affected by other components because of its Naive nature.
3. It tends to work efficiently with both continuous and discrete types of datasets and is well-versed in handling categorical features in data.
4. When we have a dataset with very less training data, then we can call up the Naive Bayes classifier in this scenario it outperforms other models.

3. What disadvantages of Naive Bayes can make you remove it from your analysis?

4. Is feature scaling required in Naive Bayes?

5. Impact of missing values on naive Bayes?

6. Impact of outliers?

7. What are different problem statements you can solve using Naive Bayes?

8. Does Naive Bayes fall under the category of the discriminative or generative classifier?

9. What do you know about posterior and prior probability in Naive Bayes

10. How does Naive Bayes treats categorical and numerical values?

Conclusion

So we are in the last section of this article and have reached here after completing the top 10 interview questions on the NB classifier. This segment usually briefly discusses everything so we can list our learnings in a nutshell.

1. Firstly we started this small journey by introducing the concept behind the Naive Bayes algorithm, and straight after that, we discussed the assumptions, advantages, and disadvantages of the Naive Bayes classifier.
2. Then we move on to tricky questions like, will this algorithm be affected by outliers and missing values? Or will the feature scaling is the required step while analyzing this classifier?
3. At last, we covered some more questions based on the mathematical intuition behind this algorithm, like How naive Bayes treats categorical and numerical values, what is posterior and prior probability, and last, whether the NB classifier is under a generative or discriminative category.

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