# 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.

## Feature And Text Classification Using Naive Bayes In R

2019-01-18

I wrap up my series on the Naive Bayes class of algorithms, finally writing some code along the way: Now we’re going to look at movie reviews and predict whether a movie review is a positive or a negative review based on its words. If you want to play along at home, grab the data set, […]

## Classifying Texts With Naive Bayes

2019-01-17

I continue my series on Naive Bayes with another hand-calculation post: Step two is, on the surface, pretty tough: how do we figure out if a set of words is a business phrase or a baseball phrase? We could try to think up a set of features. For example, how long is the phrase? How many unique […]

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