Gaussian Naive Bayes assumes continuous features follow a normal distribution. It is commonly used for real-valued data such as measurements or pixel intensities.
Multinomial Naive Bayes works with discrete counts, often used in text classification with word frequencies. It models the probability of each feature value given a class using a multinomial distribution.
Also worth reading: How did AI headshots revolutionize marketing strategies in 2020? · How Can Professionals Create Flawless AI Headshots Today? · How do I secure my Instagram AI headshot privacy settings?
Bernoulli Naive Bayes is designed for binary or boolean features. It considers whether a feature is present or absent rather than its frequency or magnitude.
Each variant makes different assumptions about the underlying data distribution. These assumptions affect how probabilities are estimated during training.
Gaussian is suitable for continuous variables, Multinomial for count data, and Bernoulli for binary indicators. Choosing the right variant depends on the nature of the input features.
All three apply Bayes' theorem with conditional independence between features. Despite this assumption, they perform well in many practical applications.
They differ in their likelihood functions and parameter estimation methods. This impacts model accuracy and suitability for specific tasks.