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

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.

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 4. Розділ 2

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?

Дякуємо за ваш відгук!

Секція 4. Розділ 2

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?

Дякуємо за ваш відгук!

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.

Секція 4. Розділ 2