How does the posterior probability of a class is computed by naive Bayes classifier?

How does the posterior probability of a class is computed by 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 posterior probability in naive Bayes?

A posterior probability, in Bayesian statistics, is the revised or updated probability of an event occurring after taking into consideration new information. The posterior probability is calculated by updating the prior probability using Bayes’ theorem.

Does class imbalance affect naive Bayes?

Large discrepancy of class ratios between training and validation sets leads to the inaccurate estimation of class prior which decrease the predictive performance of naive Bayes classifier.

Is naive Bayes good for Imbalanced data?

Uncertainty Based Under-Sampling for Learning Naive Bayes Classifiers Under Imbalanced Data Sets. This problem, known also as the curse of class imbalanced in data sets, has a potential impact in the training procedure of a classifier by learning a model that will be biased in favor of the majority class.

Which type of naive Bayes classifier is suitable for imbalanced dataset?

Complement Naive Bayes — This is basically an adaptation of the multinomial naive bayes that is particularly suited for imbalanced datasets. Bernoulli Naive Bayes — This classifier is also analogous to multinomial naive bayes but instead of words, the predictors are Boolean values.

How to calculate posterior probability in naive Bayes classifier?

Naive Bayes classifier calculates the probability of an event in the following steps: Step 1: Calculate the prior probability for given class labels Step 2: Find Likelihood probability with each attribute for each class Step 3: Put these value in Bayes Formula and calculate posterior probability.

Which is the best algorithm for Bayes classification?

Naive Bayes is a statistical classification technique based on Bayes Theorem. It is one of the simplest supervised learning algorithms. Naive Bayes classifier is the fast, accurate and reliable algorithm. Naive Bayes classifiers have high accuracy and speed on large datasets.

How is the Gaussian naive Bayes classifier used?

Naive Bayes classifier is successfully used in various applications such as spam filtering, text classification, sentiment analysis, and recommender systems. It uses Bayes theorem of probability for prediction of unknown class.

How to visualize posterior classification probabilities in MATLAB?

This example shows how to visualize posterior classification probabilities predicted by a naive Bayes classification model. Load Fisher’s iris data set. X is a numeric matrix that contains two petal measurements for 150 irises. Y is a cell array of character vectors that contains the corresponding iris species.