Scikit-Learn Random Forest regression: mix two sets of true values (y)
The problem is that an algorithm alone doesn't know which label is better. 
What you could do: Train a classifier on data which you know is correct. Use the clasifier to predcit a value for each datapoint. Compare this value to the two list of labels which you already have and choose the label which is closer. 
This solution obviously isn't perfect since the results depends on quality of the classfier which predicts the value and you still need enough labeled data to train the classifier. Additionaly there is also a chance that the classifier itself predicts a better value compared to your two lists of labels.
I am training Random Forests with two sets of "true" y values (empirical). I can easy tell which one is better.
However, I was wondering if there is a simple method, other than brute force, to pick up the values from each set that would produce the best model. In other words, I would like to automatically mix both y sets to produce a new ideal one.
Say, for instance, biological activity. Different experiments and different databases provide different values. This is a simple example showing two different sets of y values on columns 3 and 4.

4a50,DQ7,47.6,45.4
3atu,ADP,47.7,30.7
5i9i,5HV,47.7,41.9
5jzn,GUI,47.7,34.2
4bjx,73B,48.0,44.0
4a6c,QG9,48.1,45.5

I know that column 3 is better because I have already trained different models against each of them and also because I checked a few articles to verify which value is correct and 3 is right more often than 4. However, I have thousands of rows and cannot read thousands of papers.
So I would like to know if there is an algorithm that, for instance, would use 3 as a base for the true y values but would pick values from 4 when the model improves by so doing.
It would be useful it it would report the final y column and be able to use more than 2, but I think I can figure out that.
The idea now is to find out if there is already a solution out there so that I don't need to reinvent the wheel.
Best,
Miro
NOTE: The features (x) are in a different file.