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I have been trying to get a working LightGBM model which I can train on my data, select the best performing model with highest f1 score and then use it obtain the f1 score on the testing data. However, all the material I have found online gives me errors.

Currently, what I am doing is after splitting the data and test set (70:30), and preparing cv folds, I prepare the recipe, then the tuning parameters etc., eventually examining the models for parameter selection using F1 scores.

I have data which I want to use to predict the loan_default status and whether someone will default or not, using 21 predictors in my data set, using downsampling.

Does anyone have a working script or helpful resources?

Thank you in advance.

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I've found that there are usually a lot of full solution code notebooks available in Kaggle for these sorts of problems. Here's a couple I was able to find based on a quick Google search:

Hope this helps.

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