📕 subnode [[@flancia.org/pearson]]
in 📚 node [[pearson]]

I didn't know about [Pearson correlation coefficient][1] until today. It seems like such a useful thing. From Wikipedia:

- It is a measure of the linear correlation between two variables X and Y.
- It has a value between +1 and −1, where 1 is total positive linear correlation, 0 is no linear correlation, and −1 is total negative linear correlation.

So, if you have points in the plane, it can tell you how well they match `y = x`

(that yields 1) or `y = -x`

(yields -1) or neither. In ML, it can tell you how likely it is that two features (variables/"input columns") are not independent; and how likely it is that a feature will add information to a model (that happens if `Pearson(feature, target)`

is either close to -1 or 1).

I'm glad I know about it now.

[1]: https://en.wikipedia.org/wiki/Pearson_correlation_coefficient

📖 stoas

- public document at doc.anagora.org/pearson
- video call at meet.jit.si/pearson