Measures linear correlation between two variables. The resulting value lies in [-1;1], with -1 meaning perfect negative correlation (as one variable increases, the other decreases), +1 meaning perfect positive correlation and 0 meaning no linear correlation between the two variables.
Lower noise (0.71824836862138386, 7.3240173129992273e-49)
Higher noise (0.057964292079338148, 0.31700993885324746)
Use sklearn, pipeline to get the job faster.
Major Drawback of Pearson correlation as a feature ranking mechanism is that it is only sensitive to a linear relationship. If the relation is non-linear, Pearson correlation can be close to zero even if there is a 1-1 correspondence between the two variables.
For example, a correlation between x and x2 is zero or when x is centered on 0.
Pearson Correlation Chart