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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 Stacking Regressor

Stacking Regressor is a stacking ensemble learning model used to solve regression tasks. The principle of work of this model is similar to the Stacking Classifier: the only difference is that we use regression algorithms as the base and meta-models of the ensemble.
We can use the StackingRegressor class from the sklearn library to implement this model in Python.

Task

The make_friedman1 dataset is a synthetic dataset frequently used for regression tasks in machine learning. This dataset is widely used in regression tutorials and experimentation because it's simple, yet it can be customized with different noise levels and feature dimensions to simulate various regression scenarios.

Your task is to solve the regression task on the Friedman dataset using Stacking Regressor:

  1. Provide split on train and test subsets of the training set: the proportion of the dataset to be included in the test split must be 0.2.
  2. Use Decision Tree Regressor with max_depth equals 3 as one of the base models.
  3. Create a Stacking Regressor model using base models and meta model.
  4. Fit Stacking Regressor model on the training data.

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Section 4. Chapter 3
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bookChallenge: Solving Task Using Stacking Regressor

Stacking Regressor is a stacking ensemble learning model used to solve regression tasks. The principle of work of this model is similar to the Stacking Classifier: the only difference is that we use regression algorithms as the base and meta-models of the ensemble.
We can use the StackingRegressor class from the sklearn library to implement this model in Python.

Task

The make_friedman1 dataset is a synthetic dataset frequently used for regression tasks in machine learning. This dataset is widely used in regression tutorials and experimentation because it's simple, yet it can be customized with different noise levels and feature dimensions to simulate various regression scenarios.

Your task is to solve the regression task on the Friedman dataset using Stacking Regressor:

  1. Provide split on train and test subsets of the training set: the proportion of the dataset to be included in the test split must be 0.2.
  2. Use Decision Tree Regressor with max_depth equals 3 as one of the base models.
  3. Create a Stacking Regressor model using base models and meta model.
  4. Fit Stacking Regressor model on the training data.

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 4. Chapter 3
toggle bottom row

bookChallenge: Solving Task Using Stacking Regressor

Stacking Regressor is a stacking ensemble learning model used to solve regression tasks. The principle of work of this model is similar to the Stacking Classifier: the only difference is that we use regression algorithms as the base and meta-models of the ensemble.
We can use the StackingRegressor class from the sklearn library to implement this model in Python.

Task

The make_friedman1 dataset is a synthetic dataset frequently used for regression tasks in machine learning. This dataset is widely used in regression tutorials and experimentation because it's simple, yet it can be customized with different noise levels and feature dimensions to simulate various regression scenarios.

Your task is to solve the regression task on the Friedman dataset using Stacking Regressor:

  1. Provide split on train and test subsets of the training set: the proportion of the dataset to be included in the test split must be 0.2.
  2. Use Decision Tree Regressor with max_depth equals 3 as one of the base models.
  3. Create a Stacking Regressor model using base models and meta model.
  4. Fit Stacking Regressor model on the training data.

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!

Stacking Regressor is a stacking ensemble learning model used to solve regression tasks. The principle of work of this model is similar to the Stacking Classifier: the only difference is that we use regression algorithms as the base and meta-models of the ensemble.
We can use the StackingRegressor class from the sklearn library to implement this model in Python.

Task

The make_friedman1 dataset is a synthetic dataset frequently used for regression tasks in machine learning. This dataset is widely used in regression tutorials and experimentation because it's simple, yet it can be customized with different noise levels and feature dimensions to simulate various regression scenarios.

Your task is to solve the regression task on the Friedman dataset using Stacking Regressor:

  1. Provide split on train and test subsets of the training set: the proportion of the dataset to be included in the test split must be 0.2.
  2. Use Decision Tree Regressor with max_depth equals 3 as one of the base models.
  3. Create a Stacking Regressor model using base models and meta model.
  4. Fit Stacking Regressor model on the training data.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 4. Chapter 3
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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