What is main difference between Bernoulli Naive Bayes & Gaussian naive Bayes classifier?

2 Answers. We use algorithm based on the kind of dataset we have. 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 the difference between Multinomialnb and Gaussiannb?

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…

Is Gaussian naive Bayes Linear?

Naive Bayes is a linear classifier.

What is better than naive Bayes?

Decision trees work better with lots of data compared to Naive Bayes. Naive Bayes is used a lot in robotics and computer vision, and does quite well with those tasks. Decision trees perform very poorly in those situations.

What is the difference between Bernoulli and multinomial Naive Bayes?

Difference between Bernoulli, Multinomial and Gaussian Naive Bayes. Multinomial Naïve Bayes consider a feature vector where a given term represents the number of times it appears or very often i.e. frequency. On the other hand, Bernoulli is a binary algorithm used when the feature is present or not.

What is Bernoulli Naive Bayes?

Bernoulli Naive Bayes is a variant of Naive Bayes. Naive Bayes classifier is a probabilistic classifier which means that given an input, it predicts the probability of the input being classified for all the classes. It is also called conditional probability.

Is naive Bayes parametric?

Therefore, naive Bayes can be either parametric or nonparametric, although in practice the former is more common. In machine learning we are often interested in a function of the distribution T(F), for example, the mean.

What is the difference between multinomial and Bernoulli Naive Bayes?

Why do Multinomials Naive Bayes?

Multinomial Naive Bayes is one of the most popular supervised learning classifications that is used for the analysis of the categorical text data. Text data classification is gaining popularity because there is an enormous amount of information available in email, documents, websites, etc. that needs to be analyzed.

What is Gaussian naive Bayes and why is it useful?

Gaussian Naive Bayes is useful when working with continuous values which probabilities can be modeled using a Gaussian distribution: A multinomial distribution is useful to model feature vectors where each value represents, for example, the number of occurrences of a term or its relative frequency.

What is a naive Bayes model?

The general term Naive Bayes refers the the strong independence assumptions in the model, rather than the particular distribution of each feature. A Naive Bayes model assumes that each of the features it uses are conditionally independent of one another given some class.

What is a multinomial naive Bayes distribution?

The term Multinomial Naive Bayes simply lets us know that each p ( f i | c) is a multinomial distribution, rather than some other distribution. This works well for data which can easily be turned into counts, such as word counts in text.

What is conditional probability and naive Bayes?

But before we dive deep into Naïve Bayes and Gaussian Naïve Bayes, we must know what is meant by conditional probability. We can understand conditional probability better with an example. When you toss a coin, the probability of getting ahead or a tail is 50\%. Similarly, the probability of getting a 4 when you roll dice with faces is 1/6 or 0.16.