(Code) Capping outliers using the IQR method | Machine Learning смотреть онлайн

#outliers #machine #learning #iqr #capping

In this tutorial, we'll understand how to use IQR method to cap outliers in a real-life dataset.

Further reading on outliers:

https://cxl.com/blog/outliers/

https://www.theanalysisfactor.com/outliers-to-drop-or-not-to-drop/

https://humansofdata.atlan.com/2018/03/when-delete-outliers-dataset/

https://statisticsbyjim.com/basics/remove-outliers/

https://www.itl.nist.gov/div898/handbook/eda/section3/eda35h.htm

https://www.quora.com/What-are-methods-to-make-a-predictive-model-more-robust-to-outliers

https://www.quora.com/Why-are-tree-based-models-robust-to-outliers

https://heartbeat.fritz.ai/how-to-make-your-machine-learning-models-robust-to-outliers-44d404067d07

http://fmwww.bc.edu/repec/bocode/t/transint.html

https://www.ctspedia.org/do/view/CTSpedia/OutLier

https://machinelearningmastery.com/model-based-outlier-detection-and-removal-in-python/

https://datascience.stackexchange.com/questions/37394/are-decision-trees-robust-to-outliers

I've uploaded all the relevant code and datasets used here (and all other tutorials for that matter) on my github page which is accessible here:

Link:

https://github.com/rachittoshniwal/machineLearning

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