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 Stacking Classifier

Завдання

The 'blood-transfusion-service-center' dataset is a dataset that contains information related to blood donation. It's often used as a binary classification task to predict whether a blood donor will donate blood again. The dataset includes several features that provide insights into the donor's history and characteristics.

Your task is to solve a classification task using the 'blood-transfusion-service-center'` dataset:

Use 3 different LogisticRegression models as base models. Each model must have different regularization parameters: 0.1, 1, and 10, respectively.
Use MLPClassifier as meta-model of an ensemble.
Create a base_models list containing all base models of the ensemble.
Finally, create a StackingClassifier model with specified base models and meta-model.

Все було зрозуміло?

Секція 4. Розділ 2

Зміст курсу

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

Ensemble Learning

Challenge: Solving Task Using Stacking Classifier

Завдання

Your task is to solve a classification task using the 'blood-transfusion-service-center'` dataset:

Use 3 different LogisticRegression models as base models. Each model must have different regularization parameters: 0.1, 1, and 10, respectively.
Use MLPClassifier as meta-model of an ensemble.
Create a base_models list containing all base models of the ensemble.
Finally, create a StackingClassifier model with specified base models and meta-model.

Все було зрозуміло?

Секція 4. Розділ 2