Solving Naive Bayes By Hand

2019-01-11

Trust the Process
There are three steps to the process of solving the simplest of Naive Bayes algorithms. They are:
1. Find the probability of winning a game (that is, our prior probability).
2. Find the probability of winning given each input variable: whether Josh Allen starts the game, whether the team is home or away, whether the team scores 14 points, and who the top receiver was.
3. Plug in values from our new data into the formula to obtain the posterior probability.

This is an algorithm you want to solve by hand first—it’s just that easy. Then, once you understand it, let a computer do the work for larger data sets. Also, Super Bowl 2020 because I’m the kind of overly optimistic fool required of Bills fans. Just gonna leave this link here.

Sales Predictions with Pandas

2019-06-19

Megan Quinn shows how you can use Pandas and linear regression to predict sales figures: Pandas is an open-source Python package that provides users with high-performing and flexible data structures. These structures are designed to make analyzing relational or labeled data both easy and intuitive. Pandas is one of the most popular and quintessential tools leveraged […]

Linear Regression Assumptions

2019-06-17

Stephanie Glen has a chart which explains the four key assumptions behind when Ordinary Least Squares is the Best Linear Unbiased Estimator: If any of the main assumptions of linear regression are violated, any results or forecasts that you glean from your data will be extremely biased, inefficient or misleading. Navigating all of the different assumptions […]