Ensemble Learning

1. Basic Principles of Building Ensemble Models

What is an ensemble? How are ensembles different from standard machine-learning models? What are the types of ensembles? Let's consider the answers to these questions.

What is Ensemble of Models?

Bagging Models

Boosting Models

Stacking Models

2. Commonly Used Bagging Models

Let's consider some commonly used bagging ensemble models, the features of their use, and also apply some of them to solve real-life tasks.

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

The mechanism of work of boosting models differs from bagging models. Now we will explore these distinctions, gain insights into utilizing model boosting for problem-solving, and illustrate its functionality through practical demonstrations.

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

Let's consider some commonly used stacking ensemble models, the features of their use, and also apply some of them to solve real-life tasks.

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 Bagging Regressor

Tarefa

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: