Summary  
This chapter covers bootstrap aggregation (bagging), an ensemble technique that trains multiple models on different random samples drawn with replacement and averages their predictions to reduce variance.  

General domain of usage  
Machine learning classification

Bagging, short for **bootstrap aggregation**, is an **ensemble technique** that builds multiple models—most commonly **decision trees**—by training each model on a different random sample of the data. These samples are drawn **with replacement**, a process known as **bootstrap sampling**.

**Bootstrap sampling** is a statistical method where samples are drawn from the dataset with replacement, allowing the same data point to appear multiple times in a sample.

Definition

**Decision tree** is a tree-structured model used for classification or regression, where each internal node splits the data based on a feature.

By averaging the predictions of these models, **bagging** reduces variance and increases stability, particularly for high-variance models such as `DecisionTreeClassifier`. This averaging effect means that while individual models might overfit to their specific bootstrap samples, their combined output is more robust and less sensitive to the quirks of any single sample.

Which statements accurately describe bagging and bootstrap sampling in ensemble learning

A comprehensive intermediate course on ensemble learning in machine learning, covering foundational concepts, mathematical intuition, and practical implementation of bagging, boosting, and advanced integration methods using Python and scikit-learn.

Explore the foundational concepts of ensemble learning, including why and how combining multiple models leads to improved performance and robustness.

Learn the principles and implementation of bagging methods, with a focus on Random Forests and Bootstrap Aggregation.

Dive into boosting methods, including AdaBoost and Gradient Boosting, with theory, intuition, and practical implementation.

Explore the concepts and implementation of voting and stacking ensembles, and build a custom stacking ensemble using scikit-learn.

Bagging and Bootstrap Sampling

Bagging and Bootstrap Sampling