Nov-29-2018, 02:26 PM
This should be possible in Python, but I'm not sure if it's been implemented in a package yet. This is a common issue in statistics (variable selection). Common methods include forward, backward, and stepwise selection. Those depend on the statistical features of the model. However, the more statistical analysis you do on the model, the more you are reducing your theoretical degrees of freedom. I was taught that it's generally better to go to subject matter experts to get some idea of which variables are more important/likely to affect the model. Of course, this can run into the Money Ball problem of their being common biases in the "experts."
Anyway, once you understood the details of the selection methods, you could write a Python program to repeated generate the models, compare the model features (such as R-squared), select the next variable, and possibly repeat. You would need a good understanding of the statistical selection methods, and of the statistical packages in Python (numpy and pandas, at least).
Anyway, once you understood the details of the selection methods, you could write a Python program to repeated generate the models, compare the model features (such as R-squared), select the next variable, and possibly repeat. You would need a good understanding of the statistical selection methods, and of the statistical packages in Python (numpy and pandas, at least).
Craig "Ichabod" O'Brien - xenomind.com
I wish you happiness.
Recommended Tutorials: BBCode, functions, classes, text adventures
I wish you happiness.
Recommended Tutorials: BBCode, functions, classes, text adventures