Ensemble Method (Bagging Boosting)

One of the first uses of ensemble methods was the bagging technique. This technique was developed to overcome instability in decision trees. In fact, an example of the bagging technique is the random forest algorithm. The random forest is an ensemble of multiple decision trees. Decision trees tend to be prone to overfitting. Because of this, a single decision tree can’t be relied on for making predictions. To improve the prediction accuracy of decision trees, bagging is employed to form a random forest. The resulting random forest has a lower variance compared to the individual trees.

The success of bagging led to the development of other ensemble techniques such as boosting, stacking, and many others. Today, these developments are an important part of machine learning.

The many real-life machine learning applications show these ensemble methods’ importance. These applications include many critical systems. These include decision-making systems, spam detection, autonomous vehicles, medical diagnosis, and many others. These systems are crucial because they have the ability to impact human lives and business revenues. Therefore ensuring the accuracy of machine learning models is paramount. An inaccurate model can lead to disastrous consequences for many businesses or organizations. At worst, they can lead to the endangerment of human lives.

1. Ensemble Method (Bagging Boosting)

2. Agenda Ensemble Learning Bagging Boosting Conclusion

3. Ensemble Learning What is Ensemble Learning? How Ensemble Learning works? How Ensemble Learning comes to existence? 3

4. Ensemble Learning Ensemble learning in machine learning refers to the technique of combining predictions from multiple models to improve overall performance and accuracy. By aggregating diverse models such as decision trees or neural networks, ensemble methods like bagging and boosting enhance robustness, reduce overfitting, and yield more accurate and stable predictions for various tasks.

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6. Bagging

7. What is Bagging

8. Dataset Model 1 Model 2 Model n Dataset 1 Dataset 2 Dataset n Ensemble Model

9. Advantages of Bagging

10. What is boosting?

11. Training Set Subset 1 Subset 2 Subset n Weak Learner Weak Learner Weak Learner False prediction False prediction Overall Prediction Training Training Training Testing Testing

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13. Advantage of Boosting

14. Differences Between Bagging and Boosting 14 Criteria Bagging Boosting Predictions data type The simplest way of combining predictions, Which belong to the same type . A way of combining predictions that  belong to the different types . Focuses areas Aim to decrease variance , not bias. Aim to decrease bias , not variance. Weights Each model receives equal weight. Models are weighted according to their performance . Models working method Each model is built independently . New models are influenced  by the performance of previously built models . Classifier training type In this base classifiers are trained parallelly . I n this base classifiers are trained sequentially . Example The Random forest model uses Bagging. The AdaBoost uses Boosting techniques.

15. Real World Application of Bagging Real World Application of Boosting

16. Summary Enhanced Accuracy and Stability Robustness to Data Challenges Continuous Improvement and Exploration

17. Thank you

18. References: 18 ✔✔ https://www.simplilearn.com/tutorials/machine-learning-tutorial/bagging-in-machine-learning ✔✔ https://www.analyticsvidhya.com/blog/2023/01/ensemble-learning-methods-bagging-boosting-and stacking/ ✔✔ https://www.simplilearn.com/tutorials/machine-learning-tutorial/what-is-boosting ✔✔ https://www.geeksforgeeks.org/bagging-vs-boosting-in-machine-learning/ ✔✔ https://olympus.mygreatlearning.com/courses/61356