One assumption taken is the strong independence assumptions between the features. Naive Bayes Classifiers are based on the Bayes Theorem. Being a powerful tool in the study of probability, it is also applied in Machine Learning.īayes Theorem has widespread usage in variety of domains. Bayes Theoremīayes Theorem can be used to calculate conditional probability. Complex classification problems can also be implemented by using Naive Bayes Classifier. They find use when the dimensionality of the inputs is high. It is a simple classification technique, but has high functionality. Naive Bayes are a group of supervised machine learning classification algorithms based on the Bayes theorem. We have explored the idea behind Gaussian Naive Bayes along with an example.īefore going into it, we shall go through a brief overview of Naive Bayes. Gaussian Naive Bayes is a variant of Naive Bayes that follows Gaussian normal distribution and supports continuous data.
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