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 Classifier

Task

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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:

Split data into train and test sets.
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

Challenge: Solving Task Using AdaBoost Classifier

Task

Swipe to show code editor

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

Split data into train and test sets.
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 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 2

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