Part 7/11:
Boosting is another ensemble learning method that significantly enhances accuracy by iteratively training models. Each new model focuses on correcting the mistakes made by the preceding ones, refining predictions through a step-by-step learning process. The predictions from the various models are ultimately combined using weighted averaging for numerical outcomes or weighted voting for categorical labels.
Steps to Perform Boosting
The process of performing boosting consists of several steps:
Start with Equal Weights: Initial training focuses on all data points equally.
Identify Errors: Evaluate which predictions were incorrect and increase the weights of these samples, amplifying their importance for subsequent models.