In this article we will understand which is the most import things you need to learn in Naive Baye’s Algorithm which will help you perform well in the interviews.

The first thing you need to focus on is the theoretical understanding of the Naive Baye’s Algorithm

Naive Baye’s algorithm solve classification based problem statement.

Below is the video you need to watch to excel the theoretical understanding

You also need to understand how Naive Baye’s Classifier works with text data.

##### 1. What Are the Basic Assumption?

Features Are Independent

##### 2. Advantages

- Work Very well with many number of features
- Works Well with Large training Dataset
- It converges faster when we are training the model
- It also performs well with categorical features

##### 3. Disadvantages

- Correlated features affects performance

##### 4. Whether Feature Scaling is required?

No

##### 5. Impact of Missing Values?

Naive Bayes can handle missing data. Attributes are handled separately by the algorithm at both model construction time and prediction time. As such, if a data instance has a missing value for an attribute, it can be ignored while preparing the model, and ignored when a probability is calculated for a class value tutorial.

##### 6. Impact of outliers?

It is usually robust to outliers

##### Different Problem statement you can solve using Naive Baye’s

- Sentiment Analysis
- Spam classification
- twitter sentiment analysis
- document categorization

Download the github material from here

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