**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.

Ensemble Learning is an advanced machine learning technique that combines multiple models to improve overall predictive performance and decision-making when solving real-life tasks.

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.

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.

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.

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.

Challenge: Solving Task Using AdaBoost Regressor

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