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I don't have too much experience with pycaret but I'm figuring things out slowly but surely. I'm trying to figure out how to have a model predict a value, not in the data I give it but rather outside the data. For example, I have data that is ordered by date from 2020-9-30 to 2020-10-30. Once I provide the model with this data, it predicts values from 2020-9-30 to 2020-10-30, but I want it to predict 2020-10-31. I can't figure out how to make that work. Please can someone who knows how to solve this problem, if there is a solution, help me.

P.S If that was confusing, please ask me to clarify Thanks!

This is what I'm using to predict the values, asset is a 250x7 data frame. When the code runs, it prints a 250x2 data frame that is an exact copy of the asset data. But I want it to print one new value in the label column


modelpredict = predict_model(final_bestmodel, data=asset)

mpdf = pd.DataFrame(modelpredict, columns = ['Date', 'Label'])

mpdf
Jayden Swift
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1 Answers1

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If you do classification or regression you can use predict_model(best_model, data=data_unseen) and pass new data to the data argument e.g. if you want to prediction value on 2020-10-31 you need to provide all variables that you use in the training phase to model. You can find documentation from here and tutorial from here

If you do forecasting you can also use predict_model(best_model, fh = 24) and pass the number of forecasting horizons to fh argument, and don't forget to finalize the model before the last prediction. It will forecast fh step beyond the latest date in training data. You can find documentation from here and tutorial from here

Tatchai S.
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