What is Naive Bayes classifier 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.

How does naive Bayes algorithm work example?

In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. For example, a fruit may be considered to be an apple if it is red, round, and about 3 inches in diameter.

What is Naive Bayes clustering?

Naive Bayes is a kind of mixture model that can be used for classification or for clustering (or a mix of both), depending on which labels for items are observed. … Naive Bayes classification and clustering can be applied to any data with multinomial structure.

What is Naive Bayes smoothing?

Conclusion. Laplace smoothing is a smoothing technique that helps tackle the problem of zero probability in the Naïve Bayes machine learning algorithm. Using higher alpha values will push the likelihood towards a value of 0.5, i.e., the probability of a word equal to 0.5 for both the positive and negative reviews.

Is Naive Bayes supervised or unsupervised?

Naive Bayes classification is a form of supervised learning. It is considered to be supervised since naive Bayes classifiers are trained using labeled data, ie. … This contrasts with unsupervised learning, where there is no pre-labeled data available.

What is Gaussian Naive Bayes classifier?

Gaussian Naive Bayes is a variant of Naive Bayes that follows Gaussian normal distribution and supports continuous data. … Naive Bayes are a group of supervised machine learning classification algorithms based on the Bayes theorem. It is a simple classification technique, but has high functionality.

What is the benefit of Naive Bayes?

Advantages. It is easy and fast to predict the class of the test data set. It also performs well in multi-class prediction. When assumption of independence holds, a Naive Bayes classifier performs better compare to other models like logistic regression and you need less training data.