Naive Bayes Classification (Part 1): How to Teach Machines to Differentiate Genders?(Practical Data Analysis 14)
Discover the principles of Bayes' Theorem and Naive Bayes classification, exploring its real-life applications, probability concepts, and predictive modeling techniques.
Welcome to the "Practical Data Analysis" Series
Many people have heard of Bayes' Theorem. Where did you hear about it?
It’s basically when studying probability and statistics.
Some people might say, "I can't remember all these probability formulas," but don’t worry, I will try to explain them in simple, easy-to-understand language.
Bayes' Theorem was proposed by the British mathematician Thomas Bayes. Bayes was a very remarkable person, and his life story is somewhat similar to that of Van Gogh.
He wasn’t appreciated during his lifetime, but after his death, one of his papers on inductive reasoning was discovered by a friend and published.
The publication of this paper was a turning point. The ideas in this paper directly influenced statistics for the next two centuries, making it one of the most famous papers in the history of science.
Bayes' Theorem is closely related to our daily lives. For example, if you see someone constantly spending money, you might infer that this person is probably wealthy.
Of course, this is not absolute. In other words, when you can’t accurately predict the true nature of something, you can rely on events related to that thing’s nature to make judgments. If an event happens frequently, it suggests that the characteristic is more likely to exist.