Let’s learn about one of the popular Machine Learning (ML) Algorithms. Why are Naive Bayes classifiers so fundamental to AI (Artificial Intelligence) and ML (Machine Learning)? Naive Bayes is a term that is collectively used for classification algorithms that are based on Bayes Theorem. Let’s find out.

Let’s learn about one of the popular Machine Learning Algorithms

When you start learning about artificial intelligence, one of the first things that you encounter is Naive Bayes. Why are Naive Bayes classifiers so fundamental to AI and ML? Let’s find out.

Artificial intelligence has to operate with data that, in many cases, is big but incomplete. Just like humans, the computer has to take risks and think about the future that is not certain.

Uncertainty is hard to bear for human beings. But in machine learning, there are certain algorithms that help to find your way around this limitation. The Naive Bayes machine learning algorithm is one of the tools to deal with uncertainty with the help of probabilistic methods.

Probability is a field of math that enables us to reason about uncertainty and assess the likelihood of some results or events. When you work with predictive ML modeling, you have to predict an uncertain future. For example, you may try to predict the performance of an Olympic champion during the next Olympics based on past results. Even if they have won before, that doesn’t mean they will win this time. Unpredictable factors, such as an argument with their partner or not having time for breakfast, may influence their results.

Therefore, uncertainty is integral to machine learning modeling since, well, life is complicated and nothing is perfect. The three main sources of uncertainty in machine learning are noisy data, incomplete coverage of the problem, and imperfect models.

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