What type of algorithm is naive Bayes?

Tiara Williamson
Tiara WilliamsonAnswered

The naive Bayes method is a supervised learning technique that uses Bayes’s theorem to solve classification issues. It is mostly used for text classification, including a high-dimensional training set.

The Naive-Bayes Classifier is one of the simplest and most successful classification algorithms, aiding in the development of fast Machine Learning models capable of making rapid predictions. It is a classification algorithm, meaning it makes predictions based on the likelihood of an item.

Popular applications of the naive Bayes algorithm include spam filtering, sentimental analysis, and article classification.

Neutral Gaussian Bayes

This form of the naive Bayes algorithm is used when data is continuous. All variables are assumed to have a normality test. If you have variables that lack this quality, you may want to turn them into features with normal distribution.

Multinomial Naive Bayes

This is used when the frequency is represented by the characteristics.

Assume you have a TXT file where you recover all the specific words and build numerous features, each of which represents the word count in the document. Frequency is a feature in that situation. Here, you use Multinomial Naive Bayes.

It disregards the absence of the characteristics, so if the frequency of a feature is zero, its probability of occurrence is also zero. Multinomial Naive Bayes disregards that feature. It is effective for text categorization issues.

Bernoulli Naive Bayes

This is used for binary characteristics. Instead of utilizing the periodicity of the word, discrete 1s and 0s that signify the existence or absence of a characteristic is used. In this scenario, the features will be binary, and the Bernoulli Naive Bayes will be used.

This technique also penalizes the absence of a characteristic.

Benefits of naive Bayes:

  • Easy to implement and comprehend.
  • Substantially quicker than other algorithms since it just calculates probability.
  • Often used in businesses (readily scalable).
  • It is a common solution for text categorization issues.
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