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Bagging vs. Boosting: Comparing Two Popular Ensemble Methods

June 10, 2022

Introduction

Ensemble methods are a powerful technique that combines multiple machine learning models to improve their overall performance. Two popular ensemble methods are bagging and boosting. Both of these methods aim to reduce the variance of the model by aggregating multiple models. However, they differ in their approach to do so.

Bagging

Bagging stands for Bootstrap Aggregating. It involves creating multiple subsets of the training data using bootstrap sampling and training a separate model on each subset. The final prediction is made by averaging the predictions of all the models. Since the models are trained on different subsets, there is less variance in their predictions, which results in a more stable model. Bagging works well when the base models are highly variable, such as decision trees.

One fine example of bagging is the Random Forest classifier. It creates multiple decision trees using bootstrap resampling and aggregates their predictions to form the final prediction.

Boosting

Boosting is an iterative approach to ensemble learning. Unlike bagging, boosting creates multiple models sequentially, where each model tries to correct the mistakes of the previous model. The models are trained on weighted samples where the samples that were misclassified by previous models are given more weightage. The final prediction is made based on the weighted average of the predictions of all models.

One popular boosting algorithm is AdaBoost (Adaptive Boosting). In AdaBoost, each model is assigned a weight based on its accuracy, and the final prediction is made by weighing the prediction of each model.

Comparison

The primary difference between the two methods is that bagging reduces variance, while boosting reduces bias. Bagging creates multiple models that are equally good or bad and then aggregates their predictions. In contrast, boosting creates a sequence of models to add more weight to problematic training examples.

Bagging is known to work well with models that overfit training data, and boosting is effective with models that underfit. For example, bagging works favorably for decision trees while boosting may be used with linear models.

In terms of accuracy, boosting usually outperforms bagging. However, bagging is less prone to overfitting, leading to more stable predictions. Moreover, bagging is simpler to implement compared to boosting and works well with parallel processing.

Conclusion

In summary, both bagging and boosting are effective ensemble methods used to reduce variance and bias in machine learning algorithms. Both techniques have their advantages and limitations, and the choice between them depends on the type of data and the model used. Bagging reduces variance, while boosting reduces bias. However, boosting generally outperforms bagging in terms of accuracy.

References

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