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My goal is to build a classification model in order to predict if a customer will buy a product or not (binary classification).

Since in the last months (let's say 3-4) I know that the advertising of the company is changed a bit, I want to put more emphasis on the newer records.

I know that it is possible to specify the sample_weights parameter in most of the classification algorithms, but I don't know how to properly build these weights.

Basically, I have the date in which the customers have (or have not) bought the product, but I want to understand if there is a specific way to build the weights using this information.

In addition, I would also know if someone knows some references or applications for this application (time-related sampling weights).

Thank you in advance!

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  • $\begingroup$ Have you heard about adversarial validation? kaggle.com/tunguz/elo-adversarial-validation It basically implies training a model that helps you classify between training and testing samples. I think you could apply that to your problem, and use the predictions of the adversarial model that predicts last months vs older data as the weights of your samples $\endgroup$ – David Masip Jul 31 '20 at 10:32
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    $\begingroup$ Not sure what is the best solution here, nonetheless, a thing for you to have in mind is that you can also oversample the more recent instances or inversely downsample the older ones, you get the idea. For some algorithms, this works better than setting weights, or maybe it's not even possible. $\endgroup$ – 20roso Jul 31 '20 at 19:58
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One option is incremental training. Update the model weights as more recent data is available. This automatically assigns more importance to recent records.

Incremental training works well within the Bayesian framework where historical data is the prior and more recent data updates the prior.

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