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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 AdaBoost Classifier

Task

The load_wine dataset is a classic example used for classification tasks. It consists of 178 samples, each representing a different wine cultivar. The dataset comprises 13 numerical attributes that describe various chemical characteristics of the wines, including features like alcohol content, malic acid concentration, and ash content. The target variable consists of three distinct classes representing the three different cultivars.

Your task is to use AdaBoost Classifier to solve the classification problem on the load_wine dataset:

  1. Split data into train and test sets.
  2. Use the AdaBoostClassifier() constructor to create the model with 50 base estimators.

    Note

    If we don't specify the base model of AdaBoostClassifer, the Decision Tree Classifier will be used by default.

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Section 3. Chapter 2
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bookChallenge: Solving Task Using AdaBoost Classifier

Task

The load_wine dataset is a classic example used for classification tasks. It consists of 178 samples, each representing a different wine cultivar. The dataset comprises 13 numerical attributes that describe various chemical characteristics of the wines, including features like alcohol content, malic acid concentration, and ash content. The target variable consists of three distinct classes representing the three different cultivars.

Your task is to use AdaBoost Classifier to solve the classification problem on the load_wine dataset:

  1. Split data into train and test sets.
  2. Use the AdaBoostClassifier() constructor to create the model with 50 base estimators.

    Note

    If we don't specify the base model of AdaBoostClassifer, the Decision Tree Classifier will be used by default.

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 3. Chapter 2
toggle bottom row

bookChallenge: Solving Task Using AdaBoost Classifier

Task

The load_wine dataset is a classic example used for classification tasks. It consists of 178 samples, each representing a different wine cultivar. The dataset comprises 13 numerical attributes that describe various chemical characteristics of the wines, including features like alcohol content, malic acid concentration, and ash content. The target variable consists of three distinct classes representing the three different cultivars.

Your task is to use AdaBoost Classifier to solve the classification problem on the load_wine dataset:

  1. Split data into train and test sets.
  2. Use the AdaBoostClassifier() constructor to create the model with 50 base estimators.

    Note

    If we don't specify the base model of AdaBoostClassifer, the Decision Tree Classifier will be used by default.

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_wine dataset is a classic example used for classification tasks. It consists of 178 samples, each representing a different wine cultivar. The dataset comprises 13 numerical attributes that describe various chemical characteristics of the wines, including features like alcohol content, malic acid concentration, and ash content. The target variable consists of three distinct classes representing the three different cultivars.

Your task is to use AdaBoost Classifier to solve the classification problem on the load_wine dataset:

  1. Split data into train and test sets.
  2. Use the AdaBoostClassifier() constructor to create the model with 50 base estimators.

    Note

    If we don't specify the base model of AdaBoostClassifer, the Decision Tree Classifier will be used by default.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 3. Chapter 2
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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