Knowledge and Data Management White Papers
Identifying and Overcoming Common Data Mining Mistakes
Overview Due to the large amount of data typically involved, data mining analyses can exacerbate some common modeling problems and create a number of new ones. These problems can greatly increase the time that it takes to develop useful models and can hamper the development of potentially superior models. This paper discusses how to identify and overcome several common modeling mistakes. The paper begins by providing insights into common mistakes in data preparation; it then follows the data flow of a typical predictive modeling analysis through setting variable roles, creating and using data partitions, performing variable selection, replacing missing values, building different types of models, comparing resulting models, and scoring those models using SAS Enterprise Miner.
| Publisher | SAS Institute | File Format | |
|---|---|---|---|
| Date Published | May 2007 | ||
| Format | White Papers | ||
| Topics | |||
-
Did you find this white paper useful? 2 out of 5 users found this white paper useful
White Paper RSS
Download
Register
