Part 5/11:
Model Training: Train distinct models on each bootstrap sample.
Prediction Generation: Each model generates predictions based on the test dataset.
Combining Predictions: Results are combined using majority voting for classification tasks or averaging for regression tasks.
Evaluation: Assess performance using metrics such as accuracy, F1 score, or mean squared error.
Hyperparameter Tuning: Optimize the models using cross-validation where necessary.
Deployment: Finally, deploy the assembled model for real-world predictions.
Benefits of Bagging
Bagging boasts several notable advantages:
- Reduces Variance: Combining diverse models stabilizes predictions and reduces variability.