Bob Smith sits upright on the sofa as his grandson, Louis Brow, prepares to quiz him on youth slang. We are sitting in the living room of Louis's family home in Ilkley, West Yorkshire; Bob has travelled over from Walton-le-Dale, Lancashire, in his Nissan Micra. "Would you know what flexing is?" Louis begins. "If I was to flex on the Gram?" "You're bending, you're a contortionist," suggests Bob, gamely. "Nowadays it's someone showing off," Louis explains. I might say to my mate, that's a big flex, you're flexing, you're looking good." "Why not say the correct word?" Bob might not know the terminology, but he has had a major flex on social media recently. Louis is the Yorkshire Challenge belt (69kg) boxing champion and credits his success to the outdoor boxing gym, nicknamed the Dojo, that Grandad Bob helped him build in the back yard of the family home. A TikTok video of Louis using a tyre as a punchbag while his grandfather eggs him on went viral this year. Today, Louis confidently reels off his social media wins to Bob. He just hit 1m likes on TikTok; their video garnered 2.4m views. "Well, people like watching things," Bob says, sagely. Talk turns to another modern phenomenon: dating apps. Bob explains what dating was like in his youth. "There would be these dos in the church with disco dancing.

Naive, Yet it is one of the very simple yet powerful and easy to implement algorithm used in Supervised learning mainly for classification problems. Through this blog, I intend to make y'all have a basic understanding of the Naive Bayes classifier and its applications and why is it called naive. Naive Bayes classifier is built upon three main fundamental theories which are Probababilty, Conditional Probability and Bayes theorem. I assume that my readers have knowledge of these if not you can find my blog on these over here. I will just try to give an overview of what is Baye's theorem because that is important to understand Naive Baye's theorem.

There are many solutions proposed for classification purposes. Most of them share one common approach. Calculate the probability that a given sample belongs to a specific class. After that it is more subjective to decide if the given probability is an indication of class membership which is derived by cut-off threshold. This threshold is mainly determined by the utility function or risk-aversion policies.

A myriad of options exist for classification. That said, three popular classification methods-- Decision Trees, k-NN & Naive Bayes--can be tweaked for practically every situation. Naive Bayes and K-NN, are both examples of supervised learning (where the data comes already labeled). Decision trees are easy to use for small amounts of classes. If you're trying to decide between the three, your best option is to take all three for a test drive on your data, and see which produces the best results.

Caveat: It uses the worst possible technique for spam filtering: Naive Bayes, responsible for extremely poor spam filtering systems with tons of false positives and false negatives, still alive today. So this is definitely not a good resource to learn data science, but a great tutorial to learn Python, especially since naive Bayes is extremely easy to implement, though alternate but far better techniques such as hidden decision trees, are almost just as easy to code.

Despite its simplicity, the naive Bayes classifier has surprised machine learning researchers by exhibiting good performance on a variety of learning problems. Encouraged by these results, researchers have looked to overcome naive Bayes primary weakness - attribute independence - and improve the performance of the algorithm. This paper presents a locally weighted version of naive Bayes that relaxes the independence assumption by learning local models at prediction time. Experimental results show that locally weighted naive Bayes rarely degrades accuracy compared to standard naive Bayes and, in many cases, improves accuracy dramatically. The main advantage of this method compared to other techniques for enhancing naive Bayes is its conceptual and computational simplicity.