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Challenge: Solving Task Using XGBoost | Commonly Used Boosting Models
Ensemble Learning

Challenge: Solving Task Using XGBoost

Task

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.

Everything was clear?

Section 3. Chapter 6
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Course Content

Ensemble Learning

Challenge: Solving Task Using XGBoost

Task

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.

Everything was clear?

Section 3. Chapter 6
toggle bottom row
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