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 AdaBoost Regressor

AdaBoost Regressor is an ensemble learning algorithm used for regression tasks.

The principle of work of such a regressor coincides with the principle of work of the AdaBoost Classifier. The only difference is that we use some regression algorithms (linear regression, decision tree regressor, polynomial regression, etc.) as a base model.

The AdaBoostRegressor class in Python provides tools to train the model and make predictions.

Task

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Your task is to create a model to solve the regression task on the diabetes dataset:

Use a simple Linear Regression model as the base model of an ensemble.
Create an AdaBoost Regressor model with the 50 base estimators.
Print MSE to estimate regression quality.

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Section 3. Chapter 3

Challenge: Solving Task Using AdaBoost Regressor

AdaBoost Regressor is an ensemble learning algorithm used for regression tasks.

The AdaBoostRegressor class in Python provides tools to train the model and make predictions.

Task

Swipe to show code editor

Your task is to create a model to solve the regression task on the diabetes dataset:

Use a simple Linear Regression model as the base model of an ensemble.
Create an AdaBoost Regressor model with the 50 base estimators.
Print MSE to estimate regression quality.

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 3

Challenge: Solving Task Using AdaBoost Regressor

AdaBoost Regressor is an ensemble learning algorithm used for regression tasks.

The AdaBoostRegressor class in Python provides tools to train the model and make predictions.

Task

Swipe to show code editor

Your task is to create a model to solve the regression task on the diabetes dataset:

Use a simple Linear Regression model as the base model of an ensemble.
Create an AdaBoost Regressor model with the 50 base estimators.
Print MSE to estimate regression quality.

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

Everything was clear?

Thanks for your feedback!

AdaBoost Regressor is an ensemble learning algorithm used for regression tasks.

The AdaBoostRegressor class in Python provides tools to train the model and make predictions.

Task

Swipe to show code editor

Your task is to create a model to solve the regression task on the diabetes dataset:

Use a simple Linear Regression model as the base model of an ensemble.
Create an AdaBoost Regressor model with the 50 base estimators.
Print MSE to estimate regression quality.

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

Section 3. Chapter 3

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