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I have a data set like:

did_purchase  action_1_30d action_2_20d action_2_10d ....
   False            10          20            100
   True            ....etc

Where did_purchase shows whether the customer purchased or not, and the columns indicate the volume of actions taken before the purchase (or non-purchase) event.

So, for the first row the customer did 10 of action_1 within 30 days of the purchase event, but didn't purchase in the end.

I have been using sklearn's LogisticRegression to predict the did_purchase false/true, and can get about 89% accuracy, which is nice.

However, I'd like a percentage intent score instead. So it could say user-321 has a 46% chance of purchasing in the next 10 days.

What would be a good algo/approach for this?

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  • $\begingroup$ You mention 89% accuracy. What is the distribution of class labels? Is there a class imbalance? If so, accuracy may not be the right metric here. $\endgroup$
    – Wes
    Commented Feb 11, 2019 at 18:39
  • $\begingroup$ Sorry I meant F1 is 0.89. Class labels were imbalanced 1% Yes - 99% No but I SMOTE'd them $\endgroup$
    – LittleBobbyTables
    Commented Feb 11, 2019 at 18:43

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You could use the probabilities output by LogisticRegressions predict_proba method.

Almost all classifiers give you a probability in sklearn. One exception is the support vector classifier which will give you a points distance to the decision hyperplane, which can be interpreted as a confidence (you can get probabilities for the support vector classifier, but it is through a computationally costly cross validation process).

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