# Challenge: Solving Task Using AdaBoost Regressor

**AdaBoost Regressor** is an ensemble learning algorithm used for regression tasks.

The principle of work of such a regressor coincides with the principle of work of the AdaBoost Classifier. The only difference is that we use some **regression algorithms** (linear regression, decision tree regressor, polynomial regression, etc.) as a base model.

The `AdaBoostRegressor`

class in Python provides tools to train the model and make predictions.

Task

Your task is to create a model to solve the regression task on the diabetes dataset:

- Use a simple Linear Regression model as the base model of an ensemble.
- Create an AdaBoost Regressor model with the 50 base estimators.
- Print MSE to estimate regression quality.

Everything was clear?

Course Content

Ensemble Learning

## Ensemble Learning

1. Basic Principles of Building Ensemble Models

# Challenge: Solving Task Using AdaBoost Regressor

**AdaBoost Regressor** is an ensemble learning algorithm used for regression tasks.

The principle of work of such a regressor coincides with the principle of work of the AdaBoost Classifier. The only difference is that we use some **regression algorithms** (linear regression, decision tree regressor, polynomial regression, etc.) as a base model.

The `AdaBoostRegressor`

class in Python provides tools to train the model and make predictions.

Task

Your task is to create a model to solve the regression task on the diabetes dataset:

- Use a simple Linear Regression model as the base model of an ensemble.
- Create an AdaBoost Regressor model with the 50 base estimators.
- Print MSE to estimate regression quality.

Everything was clear?