I am using 3 features
(x1, x2, x3) for binary classification. All my feature values are in 0 to 1 range (unit range).
I obtained how important each feature was in classification as follows (i.e. feature importance)
x1 --> 0.1 x2 --> 0.5 x3 --> 0.7
It is clear that feature 3 (x3) contributes the most, x2 the second and x1 the least in classification.
I also performed correlation analysis to check if my features are positively or negatively correlated with the target (y) as follows.
x1 --> positively correlated x2 --> positively correlated x3 --> negatively correlated
I am wondering if it is possible to convert my classification features into a ranking function using feature importance and correlation.
For instance, my suggestion looks as follows.
ranking_score = 0.1*x1 + 0.5*x2 + 0.7*(1/x3)
The reason for using
(1/x3) in the above equation is because it is negatively correlated with the target (y). Please let me know if my ranking_score equation is statistically correct? If not, please let me know your suggestions.
EDIT: Why ranking is important to me?
My features are related to employee details (x1, x2, x3). At first I used these 3 features to classify efficient and 'inefficient' employees. Now, I want to rank the efficient employees based on these 3 features. The above equation I proposed is to facilitate this task.
I am happy to provide more details if needed.