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Please enable JavaScript in your browser settings or update your browser.Challenge: Solving Task Using Bagging Regressor | Commonly Used Bagging 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 Bagging Regressor

Task

The load_diabetes dataset contains ten baseline variables (age, sex, BMI, average blood pressure, and six blood serum measurements) for 442 diabetes patients. The target variable is a quantitative measure of disease progression one year after baseline. This dataset is used for predicting the continuous variable, representing diabetes progression, based on the given features.

Your task is to use Bagging Regressor to solve the regression problem on load_diabetes dataset:

  1. Use a simple LinearRegression model as the base model of the ensemble.
  2. Use the BaggingRegressor class to create an ensemble.
  3. Use Mean Squared Error(MSE) to evaluate the results.

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Section 2. Chapter 4
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bookChallenge: Solving Task Using Bagging Regressor

Task

The load_diabetes dataset contains ten baseline variables (age, sex, BMI, average blood pressure, and six blood serum measurements) for 442 diabetes patients. The target variable is a quantitative measure of disease progression one year after baseline. This dataset is used for predicting the continuous variable, representing diabetes progression, based on the given features.

Your task is to use Bagging Regressor to solve the regression problem on load_diabetes dataset:

  1. Use a simple LinearRegression model as the base model of the ensemble.
  2. Use the BaggingRegressor class to create an ensemble.
  3. Use Mean Squared Error(MSE) to evaluate the results.

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 2. Chapter 4
toggle bottom row

bookChallenge: Solving Task Using Bagging Regressor

Task

The load_diabetes dataset contains ten baseline variables (age, sex, BMI, average blood pressure, and six blood serum measurements) for 442 diabetes patients. The target variable is a quantitative measure of disease progression one year after baseline. This dataset is used for predicting the continuous variable, representing diabetes progression, based on the given features.

Your task is to use Bagging Regressor to solve the regression problem on load_diabetes dataset:

  1. Use a simple LinearRegression model as the base model of the ensemble.
  2. Use the BaggingRegressor class to create an ensemble.
  3. Use Mean Squared Error(MSE) to evaluate the results.

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 load_diabetes dataset contains ten baseline variables (age, sex, BMI, average blood pressure, and six blood serum measurements) for 442 diabetes patients. The target variable is a quantitative measure of disease progression one year after baseline. This dataset is used for predicting the continuous variable, representing diabetes progression, based on the given features.

Your task is to use Bagging Regressor to solve the regression problem on load_diabetes dataset:

  1. Use a simple LinearRegression model as the base model of the ensemble.
  2. Use the BaggingRegressor class to create an ensemble.
  3. Use Mean Squared Error(MSE) to evaluate the results.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 2. Chapter 4
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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