I am experimenting a project on identifying cancer or not - Binary classification
The dataset has many columns. Here, I added correlation values between few input columns and the target column[cancer/healthy subjects] calculated using pandas
[('age', 0.043351857732763135), ('occupat', 0.012718551481541234), ('c1', 0.008842838683373164), ('c2', 0.008004032794076186), ('c3', 0.007534428151349621), ('cigar_stat', 0.007269967555618035), ... ... ]
age is the only column having high correlation with output than all other input features and which is very less
0.04335. And still there are more features having correlation value in negative direction.
While training with ResNet18/50/Inception-Resnet V2, I go through many tunings, I got very less accuracy.
I understand from this experiments, if the correlation value is very less compared to the target_column, then the training accuracy is also not improving. - Is it actually a VALID point?
Having this dataset, I have no option to remove some columns which are all having very less correlation value with output, instead only use as it is.
What are the ways to handle this situation, so that I can get the good performance in training procedure?