What is naive Bayes classification algorithm?

What is naive Bayes classification algorithm?

Naive Bayes classifiers are a collection of classification algorithms based on Bayes’ Theorem. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other. To start with, let us consider a dataset.

What is naive Bayes algorithm used for?

Naive Bayes is a probabilistic machine learning algorithm that can be used in a wide variety of classification tasks. Typical applications include filtering spam, classifying documents, sentiment prediction etc. It is based on the works of Rev. Thomas Bayes (1702�61) and hence the name.

How is naive Bayes algorithm implemented?

Naive Bayes classifier calculates the probability of an event in the following steps:

1. Step 1: Calculate the prior probability for given class labels.
2. Step 2: Find Likelihood probability with each attribute for each class.
3. Step 3: Put these value in Bayes Formula and calculate posterior probability.

Can we use naive Bayes for multiclass classification?

Naive Bayes is a classification algorithm for binary (two-class) and multiclass classification problems.

Which algorithm is used for multiclass classification?

Popular algorithms that can be used for multi-class classification include: k-Nearest Neighbors. Decision Trees. Naive Bayes.

Why is naive Bayes good for text classification?

Since a Naive Bayes text classifier is based on the Bayes’s Theorem, which helps us compute the conditional probabilities of occurrence of two events based on the probabilities of occurrence of each individual event, encoding those probabilities is extremely useful.

Why naive Bayes is called naive?

Naive Bayes (NB) is ‘naive’ because it makes the assumption that features of a measurement are independent of each other. This is naive because it is (almost) never true. Of course in practice we will rarely see many measurements with identical feature sets.

What is the main idea of naive Bayesian classification?

A naive Bayes classifier assumes that the presence (or absence) of a particular feature of a class is unrelated to the presence (or absence) of any other feature, given the class variable. Basically, it’s “naive” because it makes assumptions that may or may not turn out to be correct.

What are the assumptions of naive Bayes?

Naive Bayes is so called because the independence assumptions we have just made are indeed very naive for a model of natural language. The conditional independence assumption states that features are independent of each other given the class.

How does naive Bayes work in text classification?

The Naive Bayes classifier is a simple classifier that classifies based on probabilities of events. It is the applied commonly to text classification. Let us consider sentence classification to classify a sentence to either ‘question’ or ‘statement’. In this case, there are two classes (“question” and “statement”).

What is the difference between Bayes and naive Bayes?

Naive Bayes assumes conditional independence, P(X|Y,Z)=P(X|Z), Whereas more general Bayes Nets (sometimes called Bayesian Belief Networks) will allow the user to specify which attributes are, in fact, conditionally independent.

How Bayes theorem is used for classification?

Bayesian classification uses Bayes theorem to predict the occurrence of any event. P(Y/X) is a conditional probability that describes the occurrence of event Y is given that X is true. P(X) and P(Y) are the probabilities of observing X and Y independently of each other. This is known as the marginal probability.

How does Bayes theorem predict?

Bayes’ theorem is an accessible way of integrating probability thinking into our lives. More critical is your ability and desire to assign probabilities of truth and accuracy to anything you think you know, and then being willing to update those probabilities when new information comes in. …

What Bayesian classification is?

Bayesian classification is based on Bayes’ Theorem. Bayesian classifiers are the statistical classifiers. Bayesian classifiers can predict class membership probabilities such as the probability that a given tuple belongs to a particular class.

What is Bayes theorem in simple terms?

Bayes’ theorem, named after 18th-century British mathematician Thomas Bayes, is a mathematical formula for determining conditional probability. Bayes’ theorem provides a way to revise existing predictions or theories (update probabilities) given new or additional evidence.

What are the two main assumptions made by the naive Bayes classifier?

Naive Bayes classifier assume that the effect of the value of a predictor (x) on a given class (c) is independent of the values of other predictors. This assumption is called class conditional independence. P(c|x) is the posterior probability of class (target) given predictor (attribute).

What is naive Bayesian classification in data mining?

The Naive Bayes classification algorithm is a probabilistic classifier. It is based on probability models that incorporate strong independence assumptions. Data mining in InfoSphere™ Warehouse is based on the maximum likelihood for parameter estimation for Naive Bayes models.

How is Bayes theorem useful?

As an example, Bayes’ theorem can be used to determine the accuracy of medical test results by taking into consideration how likely any given person is to have a disease and the general accuracy of the test. Posterior probability is calculated by updating the prior probability by using Bayes’ theorem.

Why naive Bayes is a bad estimator?

On the other side naive Bayes is also known as a bad estimator, so the probability outputs are not to be taken too seriously. Another limitation of Naive Bayes is the assumption of independent predictors. In real life, it is almost impossible that we get a set of predictors which are completely independent.

How do you get a feature important in naive Bayes?

The naive bayes classifers don’t offer an intrinsic method to evaluate feature importances. Naïve Bayes methods work by determining the conditional and unconditional probabilities associated with the features and predict the class with the highest probability.