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I try to make some credit score task. I stuck in conceputal problem.

There is:

train_data (62 columns, 10339239 rows, 1250000 unique ID values [0 - 1249999]([min-max] ID values))

X_train

test_data (62 columns, 4724601 rows, 500000 unique ID values [3000000 - 3499999]([min-max] ID values))

X_test

train_target.csv (2 columns: ID and flag (flag is target variable, must be predicted), with 361870 rows, all with unique ID, [0 - 361869]([min-max] ID values))

Y_train

test_target.csv (1 column: ID, 500000 rows, all ID is unique, [3000000 - 3499999]([min-max] ID values))

Y_test

Need to obtain scores in range [0,1] for test_target.csv.

train_data and test_data has 62 columns, ID, RN, ... . Both of them are corresponds time (if datetime bigger, then RN bigger in one ID value). ID means request for credit/loan, RN means number of credit/loan in credit history. FLAG in train_target.csv means: 1-default/bankruptcy.

I have no idea, how to train model for this data. I tried to use XGBoost. Trained model has to take few records ordered by RN with same ID and give one answer FLAG in range [0, 1]. how can this be done? XGBClissifier? or regressor? Throug train_test_split or TimeSeriesSplit? Can u give me advice, please?

How to apply TimeSeriesSplit on train data?

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  • $\begingroup$ Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. $\endgroup$
    – Community Bot
    Commented Apr 15 at 12:29
  • $\begingroup$ This is multi-instance-learning; wikipedia. To say much more requires more context: what does the (id-level) target represent, and what do the rn-level rows represent? $\endgroup$
    – Ben Reiniger ♦
    Commented Apr 15 at 13:38

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