Python AdaBoost Advantages and Disadvantages of AdaBoost














































Python AdaBoost Advantages and Disadvantages of AdaBoost



Advantages and Disadvantages of AdaBoost

AdaBoost has a lot of advantages, mainly it is easier to use with less need for tweaking parameters unlike algorithms like SVM. As a bonus, you can also use AdaBoost with SVM. Theoretically, AdaBoost is not prone to overfitting though there is no concrete proof for this. It could be because of the reason that parameters are not jointly optimized stage-wise estimation slows down the learning process. To understand the math behind it in depth, you can view the previous articles.

AdaBoost can be used to improve the accuracy of your weak classifiers hence making it flexible. It has now being extended beyond binary classification and has found use cases in text and image classification as well.

A few Disadvantages of AdaBoost are :

Boosting technique learns progressively, it is important to ensure that you have quality data. AdaBoost is also extremely sensitive to Noisy data and outliers so if you do plan to use AdaBoost then it is highly recommended to eliminate them.

AdaBoost has also been proven to be slower than XGBoost.

Happy Pythoning...!!


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