## What is Naive Bayes Bernoulli?

This is used for discrete data and it works on Bernoulli distribution. The main feature of Bernoulli Naive Bayes is that it accepts features only as binary values like true or false, yes or no, success or failure, 0 or 1 and so on.

## What is the difference between Bernoulli and multinomial Naive Bayes?

Bernoulli Naive bayes is good at handling boolean/binary attributes,while Multinomial Naive bayes is good at handling discrete values and Gaussian naive bayes is good at handling continuous values.

What is naive in a Naive Bayes?

Naive Bayes algorithm (NB) is Bayesian graphical model that has nodes corresponding to each of the columns or features. It is called naive because, it ignores prior distribution of parameters and assume independence of all features and all rows. Ignoring prior has both an advantage and disadvantage.

### Is Naive Bayes and naive Bayesian same?

Well, you need to know that the distinction between Bayes theorem and Naive Bayes is that Naive Bayes assumes conditional independence where Bayes theorem does not. This means the relationship between all input features are independent . Maybe not a great assumption, but this is is why the algorithm is called “naive”.

### What is Bernoulli model?

The Bernoulli model estimates as the fraction of documents of class that contain term (Figure 13.3 , TRAINBERNOULLINB, line 8). In contrast, the multinomial model estimates as the fraction of tokens or fraction of positions in documents of class that contain term. (Equation 119).

What is Bernoulli document model?

Bernoulli document model: a document is represented by a feature vector with binary elements taking value 1 if the corresponding word is present in the document and 0 if the word is not present.

#### What is the difference between Naive Bayes and Gaussian Naive Bayes?

Naive Bayes is a generative model. (Gaussian) Naive Bayes assumes that each class follow a Gaussian distribution. The difference between QDA and (Gaussian) Naive Bayes is that Naive Bayes assumes independence of the features, which means the covariance matrices are diagonal matrices.

#### What is the difference between Gaussiannb and Multinomialnb?

Gaussian NB: It should be used for features in decimal form. GNB assumes features to follow a normal distribution. 2. MultiNomial NB: It should be used for the features with discrete values like word count 1,2,3…

Why do we use Naive Bayes?

Naive Bayes is suitable for solving multi-class prediction problems. If its assumption of the independence of features holds true, it can perform better than other models and requires much less training data. Naive Bayes is better suited for categorical input variables than numerical variables.