• I'm submitting a ...
  [/] feature request

• Summary

An AdaBoost classifier is a meta-estimator that begins by fitting a classifier on the original dataset and then fits additional copies of the classifier on the same dataset but where the weights of incorrectly classified instances are adjusted such that subsequent classifiers focus more on difficult cases.

In an effort to implement boosting models in the library, Adaboost would be an ideal first model to implement in order for me to understand how boosting works.

• Illustration

• References

• Boosting Wiki artcle: https://en.wikipedia.org/wiki/Boosting_(machine_learning)
• StatQuest explanation: https://www.youtube.com/watch?v=LsK-xG1cLYA
• Packt Video: https://www.youtube.com/watch?v=BoGNyWW9-mE
• Coursera ML on Adaboost: https://www.coursera.org/lecture/ml-classification/example-of-adaboost-in-action-um0cX
• ML from Scratch implementation: https://github.com/eriklindernoren/ML-From-Scratch/blob/master/mlfromscratch/supervised_learning/adaboost.py
• Some random implementation: https://github.com/jaimeps/adaboost-implementation
• http://ficik.github.io/nlpjs/docs/classifier_adaboost.js.html
• Python 3 adaboost: https://adataanalyst.com/machine-learning/adaboost-python-3/
• Weak Learning, Boosting, and the AdaBoost algorithm (haven't read): https://jeremykun.com/2015/05/18/boosting-census/