# Pearson

I didn’t know about Pearson correlation coefficient 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.

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