Harris Amjad takes us through a rather useful class of algorithms for classification:
As AI and Machine Learning have increased in popularity, especially Large Language Models, more professionals have explored how these systems work. Unfortunately, some put the cart before the horse, where they take on more complex algorithms before learning to pave the foundation, resulting in faded interest in the topic. This tip will introduce a simple probabilistic, yet powerful classifier, the Naïve Bayes Model, and implement it in Python.
I like using the Naive Bayes variants, despite the fact that it is not Bayesian and arguably isn’t very naive. The reason I like to use this class of algorithm is that it’s fast, easy, and gives you a useful baseline for quality. If you need to meet some specific quality threshold (say, accuracy > 85% or F1-score above 0.8), you can get an answer quickly with Naive Bayes. If that answer is anywhere near your threshold, the problem is likely solvable. If your answer is way below the threshold, it’s probably not worth spending the time or compute effort trying out a variety of other algorithms.