Bayes’ Theorem is a fundamental concept in probability theory and statistics, offering a powerful framework for updating our beliefs in light of new evidence. This theorem is particularly useful in fields like machine learning, medical diagnostics, and even everyday decision-making. But what happens when we lack certain probabilities, specifically P(B)? This post talks about how Bayes’ Theorem works and explores strategies to apply it even without direct knowledge of P(B).
This post contains some of my favorite tips and tricks for working with VSCode. For even more, I recommend VSCode’s own tips and tricks page.
This post details the customizations I have made to the software I use. I thought it might be helpful to others.
I’m always curious about how people set up their computers and what software packages they have, so I thought I would share mine. So you know where I’m coming from, I use Windows, Mac, and Linux nearly every day. Part of my goal is to make transitioning between these systems as seamless as possible, but the exact setup varies by the operating system. For those of you on Windows, many of these can be installed with Ninite. I do a pretty good job of keeping this list up-to-date, so it should reflect my current recommendations for software.
Now that we’ve cleaned and prepared the data, let’s try classifying it using logistic regression. Logistic regression is a popular machine learning algorithm for classification due to its speed and accuracy relative to its simplicity.
