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Creates an ensemble of XGBoostClassifier models by looping through a number of, user-defined, iterations using a RandomizedSearchCV to tune and cross-validate each iteration with the outcome being predicted probability of a binary outcome.

Mean probability and standard deviation of the test data across all models are calculated and model evaluation metrics are generated and programmatically saved in a folder named with the current date (year_month_day format).

Simultaneously, mean predictions over all iterations are generated on the current, unseen data and the output is saved in the same programmatically named folder.

Dependencies include:

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