Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Challenge: Solving Task Using XGBoost | Commonly Used Boosting Models
Ensemble Learning
course content

Course Content

Ensemble Learning

Ensemble Learning

1. Basic Principles of Building Ensemble Models
What is Ensemble of Models?Bagging ModelsBoosting ModelsStacking Models
2. Commonly Used Bagging Models
Bagging ClassifierChallenge: Solving Task Using Bagging ClassifierBagging RegressorChallenge: Solving Task Using Bagging RegressorRandom ForestChallenge: Determining Feature Importances Using Random ForestExtraTrees
3. Commonly Used Boosting Models
AdaBoost ClassifierChallenge: Solving Task Using AdaBoost ClassifierChallenge: Solving Task Using AdaBoost RegressorGradient BoostingXGBoostChallenge: Solving Task Using XGBoost
4. Commonly Used Stacking Models
Stacking ClassifierChallenge: Solving Task Using Stacking ClassifierChallenge: Solving Task Using Stacking RegressorUsing Ensembles As Base ModelsCourse Summary

bookChallenge: 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.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 3. Chapter 6
toggle bottom row

bookChallenge: 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.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 3. Chapter 6
toggle bottom row

bookChallenge: 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.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

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.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 3. Chapter 6
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
some-alt