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 Stacking Classifier

Task

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The 'blood-transfusion-service-center' dataset is a dataset that contains information related to blood donation. It's often used as a binary classification task to predict whether a blood donor will donate blood again. The dataset includes several features that provide insights into the donor's history and characteristics.

Your task is to solve a classification task using the 'blood-transfusion-service-center'` dataset:

Use 3 different LogisticRegression models as base models. Each model must have different regularization parameters: 0.1, 1, and 10, respectively.
Use MLPClassifier as meta-model of an ensemble.
Create a base_models list containing all base models of the ensemble.
Finally, create a StackingClassifier model with specified base models and meta-model.

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Section 4. Chapter 2

Challenge: Solving Task Using Stacking Classifier

Task

Swipe to show code editor

Your task is to solve a classification task using the 'blood-transfusion-service-center'` dataset:

Use 3 different LogisticRegression models as base models. Each model must have different regularization parameters: 0.1, 1, and 10, respectively.
Use MLPClassifier as meta-model of an ensemble.
Create a base_models list containing all base models of the ensemble.
Finally, create a StackingClassifier model with specified base models and meta-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 4. Chapter 2

Challenge: Solving Task Using Stacking Classifier

Task

Swipe to show code editor

Your task is to solve a classification task using the 'blood-transfusion-service-center'` dataset:

Use 3 different LogisticRegression models as base models. Each model must have different regularization parameters: 0.1, 1, and 10, respectively.
Use MLPClassifier as meta-model of an ensemble.
Create a base_models list containing all base models of the ensemble.
Finally, create a StackingClassifier model with specified base models and meta-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 a classification task using the 'blood-transfusion-service-center'` dataset:

Use 3 different LogisticRegression models as base models. Each model must have different regularization parameters: 0.1, 1, and 10, respectively.
Use MLPClassifier as meta-model of an ensemble.
Create a base_models list containing all base models of the ensemble.
Finally, create a StackingClassifier model with specified base models and meta-model.

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

Section 4. Chapter 2

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