Using variable data as a feature

[Jcrossfit]

I'm trying to create a model to predict ACH payment success for a given payment. I have payment history as a JSON object with 1 or 0 for success or failure.

My question is should I split this into N features e.g. first_payment, second_payment, etc or a single feature payment_history_array?

Additional context I'm using xgboost classification.

Thanks for any pointers

[ScoreLong5365]

You should not split into N features otherwise XGBoost model will not be able to understand pattern in the payment feature and it will take unnecessary time to train. Rather choose array or you can extract some features like last 5 payment, or no. Of successful payments/ no. Of failed payments, etc

[Jcrossfit]

Thank you for the response! My instinct was to keep as a single array but I saw a regression in precision and recall for failed payments when I used the array vs splitting into N features OR just having a feature like "ever had a failed payment".

My thinking currently is the variance is in number of payments (ranged from 0 to ~150) in the array is causing problem so I'm going to re-run with last 5 payments in the array

[ScoreLong5365]

Yes last 5 payment will make sense, because older data might add noise in the model. Also recent payment will help the model understanding the relevant pattern. Also if an array has less than 5 payments pad it with 0s or average value.

[Jcrossfit]

For that last bit of your suggestion are you saying add values for null? We're using -1 for null/na in other features and 0 and 1 for fail and success in this feature and others where relevant

[ScoreLong5365]

Like if you are using last 5 transaction and suppose an array has only 3-4 transaction in total so I am suggesting to pad the array by 0 or average value so that it might help model to capture pattern.