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 Regressor

Stacking Regressor is a stacking ensemble learning model used to solve regression tasks. The principle of work of this model is similar to the Stacking Classifier: the only difference is that we use regression algorithms as the base and meta-models of the ensemble.
We can use the StackingRegressor class from the sklearn library to implement this model in Python.

Завдання

Swipe to show code editor

The make_friedman1 dataset is a synthetic dataset frequently used for regression tasks in machine learning. This dataset is widely used in regression tutorials and experimentation because it's simple, yet it can be customized with different noise levels and feature dimensions to simulate various regression scenarios.

Your task is to solve the regression task on the Friedman dataset using Stacking Regressor:

Provide split on train and test subsets of the training set: the proportion of the dataset to be included in the test split must be 0.2.
Use Decision Tree Regressor with max_depth equals 3 as one of the base models.
Create a Stacking Regressor model using base models and meta model.
Fit Stacking Regressor model on the training data.

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 3

Challenge: Solving Task Using Stacking Regressor

Завдання

Swipe to show code editor

Your task is to solve the regression task on the Friedman dataset using Stacking Regressor:

Provide split on train and test subsets of the training set: the proportion of the dataset to be included in the test split must be 0.2.
Use Decision Tree Regressor with max_depth equals 3 as one of the base models.
Create a Stacking Regressor model using base models and meta model.
Fit Stacking Regressor model on the training data.

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 4. Розділ 3

Challenge: Solving Task Using Stacking Regressor

Завдання

Swipe to show code editor

Your task is to solve the regression task on the Friedman dataset using Stacking Regressor:

Provide split on train and test subsets of the training set: the proportion of the dataset to be included in the test split must be 0.2.
Use Decision Tree Regressor with max_depth equals 3 as one of the base models.
Create a Stacking Regressor model using base models and meta model.
Fit Stacking Regressor model on the training data.

Все було зрозуміло?

Дякуємо за ваш відгук!

Завдання

Swipe to show code editor

Your task is to solve the regression task on the Friedman dataset using Stacking Regressor:

Provide split on train and test subsets of the training set: the proportion of the dataset to be included in the test split must be 0.2.
Use Decision Tree Regressor with max_depth equals 3 as one of the base models.
Create a Stacking Regressor model using base models and meta model.
Fit Stacking Regressor model on the training data.

Секція 4. Розділ 3