Course Content

Ensemble Learning

1. Basic Principles of Building Ensemble Models

What is Ensemble of Models?Bagging Models Boosting Models Stacking Models

2. Commonly Used Bagging Models

Bagging Classifier Challenge: Solving Task Using Bagging Classifier Bagging Regressor Challenge: Solving Task Using Bagging Regressor Random Forest Challenge: Determining Feature Importances Using Random Forest ExtraTrees

3. Commonly Used Boosting Models

AdaBoost Classifier Challenge: Solving Task Using AdaBoost Classifier Challenge: Solving Task Using AdaBoost Regressor Gradient Boosting XGBoost Challenge: Solving Task Using XGBoost

4. Commonly Used Stacking Models

Stacking Classifier Challenge: Solving Task Using Stacking Classifier Challenge: Solving Task Using Stacking Regressor Using Ensembles As Base Models Course Summary

Challenge: Solving Task Using XGBoost

Task

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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. Specify enable_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.

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Section 3. Chapter 6

Challenge: Solving Task Using XGBoost

Task

Swipe to show code editor

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

Create Dmatrix objects using training and test data. Specify enable_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.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 3. Chapter 6

Challenge: Solving Task Using XGBoost

Task

Swipe to show code editor

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

Create Dmatrix objects using training and test data. Specify enable_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.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Task

Swipe to show code editor

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

Create Dmatrix objects using training and test data. Specify enable_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.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 3. Chapter 6

Switch to desktop for real-world practiceContinue from where you are using one of the options below