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