Part 8/11:
Train the Next Model: The new model is trained with updated weights, concentrating on the previously misclassified data.
Repeat Until Optimized: This cycle continues until the error rate drops or a predetermined number of iterations is reached.
Advantages of Boosting
Boosting presents several key advantages:
Higher Accuracy: Combines multiple weak models to yield improved predictions.
Less Overfitting: Addresses the risk of overfitting by honing in on complex data points.
Handles Imbalanced Data: Elevates the significance of misclassified data points, making it effective with skewed datasets.
Better Interpretability: The cumulative nature of boosting provides clarity on how decisions are made throughout the learning process.