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What is a Naïve Bayes Classifier?
A Naïve Bayes classifier is a probabilistic machine learning algorithm that is based on Bayes' theorem and assumes independence between the features (hence the term "naïve"). It is widely used for classification tasks, particularly in natural language processing, text classification, and spam filtering. The Naïve Bayes classifier calculates the probability of a given instance belonging to a particular class based on the probabilities of its features. It assumes that the presence or absence of each feature is independent of the presence or absence of other features, which simplifies the calculations.
On a top-level, a Naïve Bayes classifier works in the following way:
Training Phase: During the training phase, the classifier learns the probabilities of different features given each class from a labeled dataset. It calculates the prior probability of each class and the conditional probability of each feature given the class.
Feature Independence Assumption: The Naïve Bayes classifier assumes that the features are conditionally independent given the class label. This assumption allows the classifier to calculate the probability of an instance belonging to a particular class by multiplying the conditional probabilities of its features given that class.
Classification Phase: In the classification phase, the classifier uses the learned probabilities to assign a class label to a new, unseen instance. It calculates the posterior probability of each class given the instance's features using Bayes' theorem. The class with the highest posterior probability is selected as the predicted class.
The Naïve Bayes classifier is efficient, easy to implement, and performs well on large datasets. However, its independence assumption may not hold in all cases, leading to suboptimal results when the features are highly correlated. Despite this limitation, Naïve Bayes classifiers are still widely used due to their simplicity and effectiveness, particularly in text-based classification tasks. There are different variants of Naïve Bayes classifiers, such as Gaussian Naïve Bayes (for continuous numerical features), Multinomial Naïve Bayes (for discrete features), and Bernoulli Naïve Bayes (for binary features). These variants handle different types of data and are adapted to specific application domains.
Naïve Bayes classifiers are covered in more detail in module 4 of the CQF program.