Abstract
Removal of outliers is the simplest type of data filtering. Even though sometimes outliers should be kept in the data set as part of the analysis, such as in the case of modeling of credit risk, fraud, and other rare events, in most of the cases, they represent unnecessary information and should be removed. Unnecessary outliers are noise in the data and mostly can reduce the predictability of the model. This tutorial shows an automatic and manual way to remove outliers.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Handbook of Statistical Analysis and Data Mining Applications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
