Open lprzqxsnoopy opened 4 years ago
UpliftEval
class to evaluate the cumulative gains curve on any ranking. In this case, simply use your response model to predict on a test set, and initiate UpliftEval using those predictions.Thanks for answering~ I also have a question that Is qini of training set very close to qini of testing set representing model stability?
another quetion: Can I replace the model with LGBM?
another quetion: Can I replace the model with LGBM?
Think you should as long as it has an API similar to sklearn. You can specify your model using the 'sklearn_model' argument. See https://github.com/wayfair/pylift/blob/master/examples/simulated_data/sample.ipynb
I have a uplift model and have two versions of the results:
Model A: the aqini of train result is 0.4415 and the aqini of test result is 0.1675.
Model B: the aqini of train result is 0.2003 and the test result is 0.2468.
Can I regard the model A as over-fitting?
Is the model B better than model A?
Can I regard the model A as over-fitting?
Yes.
Is the model B better than model A?
Yes, probably, but you should look at the cumulative gains curve is higher for model B for the proportion of people you want to target.
hello, I have two questions and hope that you can help me. 1) I want to know when we have got an uplift model and every customer has got a score. how to compare with the reponse model . 2) AUC is a common evaluation. When AUC reaches 0.8, we will think that the model works well. So, when Qini reaches what value, the model works better?