Contenido del Curso

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 Bagging Regressor

Tarea

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:

Use a simple LinearRegression model as the base model of the ensemble.
Use the BaggingRegressor class to create an ensemble.
Use Mean Squared Error(MSE) to evaluate the results.

Tarea

Your task is to use Bagging Regressor to solve the regression problem on load_diabetes dataset:

Use a simple LinearRegression model as the base model of the ensemble.
Use the BaggingRegressor class to create an ensemble.
Use Mean Squared Error(MSE) to evaluate the results.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

Sección 2. Capítulo 4

Challenge: Solving Task Using Bagging Regressor

Tarea

Your task is to use Bagging Regressor to solve the regression problem on load_diabetes dataset:

Use a simple LinearRegression model as the base model of the ensemble.
Use the BaggingRegressor class to create an ensemble.
Use Mean Squared Error(MSE) to evaluate the results.

Tarea

Your task is to use Bagging Regressor to solve the regression problem on load_diabetes dataset:

Use a simple LinearRegression model as the base model of the ensemble.
Use the BaggingRegressor class to create an ensemble.
Use Mean Squared Error(MSE) to evaluate the results.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

Sección 2. Capítulo 4

Challenge: Solving Task Using Bagging Regressor

Tarea

Your task is to use Bagging Regressor to solve the regression problem on load_diabetes dataset:

Use a simple LinearRegression model as the base model of the ensemble.
Use the BaggingRegressor class to create an ensemble.
Use Mean Squared Error(MSE) to evaluate the results.

Tarea

Your task is to use Bagging Regressor to solve the regression problem on load_diabetes dataset:

Use a simple LinearRegression model as the base model of the ensemble.
Use the BaggingRegressor class to create an ensemble.
Use Mean Squared Error(MSE) to evaluate the results.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

Tarea

Your task is to use Bagging Regressor to solve the regression problem on load_diabetes dataset:

Use a simple LinearRegression model as the base model of the ensemble.
Use the BaggingRegressor class to create an ensemble.
Use Mean Squared Error(MSE) to evaluate the results.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

Sección 2. Capítulo 4

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones