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Ensemble Learning
Ensemble Learning
Challenge: Solving Task Using XGBoost
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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:
- Create
Dmatrix
objects using training and test data. Specifyenable_categorical
argument to use categorical features. - Train the XGBoost model using the training
DMatrix
object. - 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.
Дякуємо за ваш відгук!
Challenge: Solving Task Using XGBoost
Swipe to show code editor
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:
- Create
Dmatrix
objects using training and test data. Specifyenable_categorical
argument to use categorical features. - Train the XGBoost model using the training
DMatrix
object. - 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.
Дякуємо за ваш відгук!
Challenge: Solving Task Using XGBoost
Swipe to show code editor
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:
- Create
Dmatrix
objects using training and test data. Specifyenable_categorical
argument to use categorical features. - Train the XGBoost model using the training
DMatrix
object. - 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.
Дякуємо за ваш відгук!
Swipe to show code editor
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:
- Create
Dmatrix
objects using training and test data. Specifyenable_categorical
argument to use categorical features. - Train the XGBoost model using the training
DMatrix
object. - 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.