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...!!

More Articles of Aditi Kothiyal:

Name Views Likes
Python AdaBoost Mathematics Behind AdaBoost 421 1
Python PyCaret How to optimize the probability threshold % in binary classification 2071 0
Python K-means Predicting Iris Flower Species 1323 2
Python PyCaret How to ignore certain columns for model building 2635 0
Python PyCaret Experiment Logging 680 0
Python PyWin32 Open a File in Excel 941 0
Python Guppy GSL Introduction 220 2
Python Usage of Guppy With Example 1102 2
Python Naive Bayes Tutorial 552 2
Python Guppy Recent Memory Usage of a Program 893 2
Introduction to AdaBoost 290 1
Python AdaBoost Implementation of AdaBoost 513 1
Python AdaBoost Advantages and Disadvantages of AdaBoost 3715 1
Python K-Means Clustering Applications 333 2
Python Random Forest Algorithm Decision Trees 440 0
Python K-means Clustering PREDICTING IRIS FLOWER SPECIES 457 1
Python Random Forest Algorithm Bootstrap 476 0
Python PyCaret Util Functions 441 0
Python K-means Music Genre Classification 1763 1
Python PyWin Attach an Excel file to Outlook 1542 0
Python Guppy GSL Document and Test Example 248 2
Python Random Forest Algorithm Bagging 387 0
Python AdaBoost An Example of How AdaBoost Works 280 1
Python PyWin32 Getting Started PyWin32 603 0
Python Naive Bayes in Machine Learning 376 2
Python PyCaret How to improve results from hyperparameter tuning by increasing "n_iter" 1724 0
Python PyCaret Getting Started with PyCaret 2.0 357 1
Python PyCaret Tune Model 1325 1
Python PyCaret Create your own AutoML software 321 0
Python PyCaret Intoduction to PyCaret 297 1
Python PyCaret Compare Models 2697 1
Python PyWin Copying Data into Excel 1154 0
Python Guppy Error: expected function body after function declarator 413 2
Python Coding Random forest classifier using xgBoost 247 0
Python PyCaret How to tune "n parameter" in unsupervised experiments 659 0
Python PyCaret How to programmatically define data types in the setup function 1403 0
Python PyCaret Ensemble Model 806 1
Python Random forest algorithm Introduction 228 0
Python k-means Clustering Example 340 1
Python PyCaret Plot Model 1245 1
Python Hamming Distance 715 0
Python Understanding Random forest algorithm 311 0
Python PyCaret Sort a Dictionary by Keys 245 0
Python Coding Random forest classifier using sklearn 341 0
Python Guppy Introduction 368 2
Python How to use Guppy/Heapy for tracking down Memory Usage 1069 2
Python AdaBoost Summary and Conclusion 232 1
Python PyCaret Create Model 365 1
Python k -means Clusturing Introduction 326 2
Python k-means Clustering With Example 351 2