I have a training data set distributed in two files.
File 1: This contains actual classification for each X1. X1 is unique in this file. X1 has one-to-one relationship with X2, i.e. X2 is also unique. Y is binary.
| X1 | X2 | Y | | 1 | 4 | 0 | | 3 | 5 | 1 | ... | 8 | 9 | 1 |
File 2: This contains the real 'observations' of the experiment. X1 can appear multiple times.
| X1 | X3 | X4 | | 3 | 4 | 5 | | 3 | 1 | 2 | ... | 1 | 4 | 8 |
Here I can combine the two tables to have a structure like below and use them as observations:
| X1 | X2 | X3 | X4 | Y | | 3 | 5 | 4 | 5 | 1 | | 3 | 5 | 1 | 2 | 1 | ... | 1 | 4 | 4 | 8 | 0 |
For test data I have similar structure, just the Y column is missing in File 1.
I have multiple concerns here:
- X1 and X2 has one-to-one dependency in the data, i.e. X1 = f(X2) and X2 = f(X1)
- Y = f'(X1) or f'(X2)
- Frequency distribution of X1,X2 and Y changes dramatically in the new joined data set.
- Does this kind of transformation of data leads to any insights?
- Does regression and ensemble learning techniques are capable of capturing these internal relationships?