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Naive Bayes

Naive Bayes classifiers are supervised machine learning algorithms used for classification tasks, based on Bayes Theorem to find probabilities. This article will give you an overview as well as more advanced use and implementation of Naive Bayes in machine learning.

Key Features of Naive Bayes Classifiers​

The main idea behind the Naive Bayes classifier is to use Bayes Theorem to classify data based on the probabilities of different classes given the features of the data. It is used mostly in high-dimensional text classification

  • The Naive Bayes Classifier is a simple probabilistic classifier and it has very few number of parameters which are used to build the ML models that can predict at a faster speed than other classification algorithms.
  • It is a probabilistic classifier because it assumes that one feature in the model is independent of existence of another feature. In other words, each feature contributes to the predictions with no relation between each other.
  • Naïve Bayes Algorithm is used in spam filtration, Sentimental analysis, classifying articles and many more.

Why it is Called Naive Bayes?​

The Naïve Bayes algorithm is comprised of two words Naïve and Bayes, Which can be described as:

  • Naïve: It is called Naïve because it assumes that the occurrence of a certain feature is independent of the occurrence of other features. Such as if the fruit is identified on the bases of color, shape, and taste, then red, spherical, and sweet fruit is recognized as an apple. Hence each feature individually contributes to identify that it is an apple without depending on each other.
  • Bayes: It is called Bayes because it depends on the principle of Bayes' Theorem.

Advantages of Naïve Bayes Classifier:​

  • Naïve Bayes is one of the fast and easy ML algorithms to predict a class of datasets.
  • It can be used for Binary as well as Multi-class Classifications.
  • It performs well in Multi-class predictions as compared to the other Algorithms.
  • It is the most popular choice for text classification problems.

Disadvantages of Naïve Bayes Classifier:​

  • Naive Bayes assumes that all features are independent or unrelated, so it cannot learn the relationship between features.

Applications of Naïve Bayes Classifier:​

  • It is used for Credit Scoring.
  • It is used in medical data classification.
  • It can be used in real-time predictions because Naïve Bayes Classifier is an eager learner.
  • It is used in Text classification such as Spam filtering and Sentiment analysis.