How would you solve this? I'd like to predict a per user event (purchase) based on count features (e.g. clicks on different ads), and I can gather those count features using different levels of filtering on a continuous scale (e.g. price advertised in the ad). How do I find good cutoffs to build the independent variables? E.g. use n clicks at $40+ vs $35+

I feel like there has to be some simple frequency based comparison I can do to find "natural" cutoffs, but my brain is scrambled

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Maybe something really simple like k-means clustering on a single variable to bucket them.

This assumes that you don’t want to bother looking at the distribution to figure it out.

You could also just split into quantiles. 0-20th percentile, 20th to 40th, etc.

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Thanks for the ideas!

How about plotting out the cumulative number of clicks, with price advertised as the X axis? Expectation would be that it’s a curve with higher slope at lower prices and flattens out as you reach higher prices, and you could eyeball a region where the curve flattens the most rapidly as a starting range for which prices to investigate further.

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Are you trying to predict a binary variable (purchased vs did not purchase) or a value (purchase amount)?

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I’ll add that I question the usefulness of bucketing these variables. Maybe run a model through xgboost/random forest with price/basket size as continuous. Pull the shap values and then plot price vs shap value and see if there are any discontinuities or if it is a linear relationship.

Many ways to tackle this problem.

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