Glossary
Naive Bayes
Naive Bayes is a popular algorithm used in machine learning to solve classification problems. It is a probabilistic algorithm that makes predictions based on the likelihood of a data point belonging to a particular class.
The "naive" part of Naive Bayes comes from the assumption that all features in the dataset are independent of each other. This means that the presence or absence of one feature has no effect on the presence or absence of another feature. Although this assumption is not always true in real-world scenarios, Naive Bayes still performs remarkably well on many datasets.
One of the key advantages of Naive Bayes is its simplicity and speed. It requires only a small amount of training data to make accurate predictions and can handle high-dimensional datasets with ease. Naive Bayes is also highly interpretable, making it a popular choice for applications where understanding how the model arrived at its decision is important.
There are three main types of Naive Bayes classifiers: Gaussian, Multinomial, and Bernoulli. Gaussian Naive Bayes is used for continuous data, while Multinomial Naive Bayes is used for discrete data such as text. Bernoulli Naive Bayes is similar to Multinomial Naive Bayes but is used for binary data.
Overall, Naive Bayes is a powerful algorithm that can be used for a wide range of classification tasks. Its simplicity, speed, and interpretability make it a popular choice for both researchers and practitioners in the field of machine learning.
A wide array of use-cases
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