The "Credit Scoring" dataset is commonly used for credit risk analysis and binary classification tasks. It contains information about customers and their credit applications, with the goal of predicting whether a customer's credit application will result in a good or bad credit outcome.

Your task is to solve classification task on "Credit Scoring" dataset:

1. Create Dmatrix objects using training and test data. Specify enable_categorical argument to use categorical features.
2. Train the XGBoost model using the training DMatrix object.
3. Set the split threshold to 0.5 for correct class detection.

Note

'objective': 'binary:logistic' parameter means that we will use logistic loss (also known as binary cross-entropy loss) as an objective function when training the XGBoost model.