tag:blogger.com,1999:blog-21831384.post114286413613349796..comments2019-10-03T14:11:35.530+05:30Comments on BzST | Business Analytics, Statistics, Teaching: Data mining for prediction vs. explanationGalit Shmuelihttp://www.blogger.com/profile/06119270323184007583noreply@blogger.comBlogger3125tag:blogger.com,1999:blog-21831384.post-1169948489652317412007-01-28T07:11:00.000+05:302007-01-28T07:11:00.000+05:30Thanks Hans-Joerg for the philoshophical angle - t...Thanks Hans-Joerg for the philoshophical angle - this definitely further supports the distinction.Galit Shmuelihttps://www.blogger.com/profile/06119270323184007583noreply@blogger.comtag:blogger.com,1999:blog-21831384.post-1143659074608992342006-03-30T00:34:00.000+05:302006-03-30T00:34:00.000+05:30That's a very good point. In short, yes, it is mor...That's a very good point. In short, yes, it is more of a concern in predictive modeling because it might be harder to spot. But here's a longer answer: <BR/><BR/>In an explanatory task overfitting means finding relationships that are just due to noise. But we do have some statistical test to avoid that: since we use methods that shed light on the relatioship between the response and predictors (Galit Shmuelihttps://www.blogger.com/profile/06119270323184007583noreply@blogger.comtag:blogger.com,1999:blog-21831384.post-1143648048109462302006-03-29T21:30:00.000+05:302006-03-29T21:30:00.000+05:30so is overfitting more of a concern when trying to...so is overfitting more of a concern when trying to predict, or should we be worried about it also in an explantory setting? <BR/><BR/>corollary: is there a scenario where you would partition the data in the process of developing an explanatory model, or does that only come into play in a prediction setting?<BR/><BR/>ravi b.Ravi Bapnahttps://www.blogger.com/profile/09872946904287163034noreply@blogger.com