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.