I used to tried making a fake correlation in my mocking dataset and found that if the score is more than
0.5 I can reduce feature to avoid singularity
In the given example they are many correlated variables. Therefore I am thinking about dimensionality reduction. The one feature here that I plan to remove is
Hum-6D05 because it has strong correlation between
Hum-6D12, Hum-6F04, and Hum-6F14
Am I correct?
Any comment or advice are welcomed