Part 4/11:
Bagging, short for bootstrap aggregating, is an ensemble learning technique designed to improve the accuracy and reliability of predictions. It operates by taking multiple random samples from the training data, where some data points may be repeated. Each sample trains a separate model (like decision trees or neural networks), and the predictions are then combined—either through averaging or majority voting—to yield a more accurate result.
Implementation Steps of Bagging
Implementing bagging involves several crucial steps:
Data Preparation: Clean and process the dataset before splitting it into training and test sets.
Bootstrap Sampling: Create multiple random samples from the training data, allowing for some data points to repeat.