Naïve Bayes is a classification model that attempts to predict if an entity belongs to a series of predefined sets. When you aggregate all the sets together, you have a pretty good estimation of the final result. In order to illustrate, let's go back to the tennis example that we used when talking about decision trees.
We have the following observations for two weeks:
Day |
Outlook |
Temperature |
Humidity |
Wind |
PlayTennis? |
---|---|---|---|---|---|
0 |
sunny |
Hot |
High |
weak |
No |
1 |
sunny |
Hot |
High |
strong |
No |
2 |
overcast |
Hot |
High |
weak |
Yes |
3 |
rain |
Mild |
High |
weak |
Yes |
4 |
rain |
Cool |
Normal |
weak |
Yes |
5 |
rain |
Cool |
Normal |
strong |
No |
6 |
overcast |
Cool |
Normal |
strong |
Yes |
7 |
sunny |
Mild |
High |
weak |
No |
8 |
sunny |
Cool |
Normal |
weak |
yes |
9 |
rain |
Mild |
Normal |
weak |
yes |
10 |
sunny |
Mild |
Normal |
strong |
yes |
11 |
overcast |
Mild |
High |
strong |
yes |
12 |
overcast |
Hot |
Normal |
weak |
yes |
13 |
rain |
Mild |
High |
strong |
no |
For each one of the classes, let's break down whether they wound up playing...