WOE v/s using continuous variables as such - AnalyticBridge2019-12-06T02:45:16Zhttps://www.analyticbridge.datasciencecentral.com/forum/topics/woe-v-s-using-continuous-variables-as-such?commentId=2004291%3AComment%3A219595&x=1&feed=yes&xn_auth=noThanks Sandeep, Sorry to have…tag:www.analyticbridge.datasciencecentral.com,2012-10-24:2004291:Comment:2195952012-10-24T20:34:25.587ZRockyRambohttps://www.analyticbridge.datasciencecentral.com/profile/VarunNakra
<p>Thanks Sandeep, Sorry to have missed your reply..Yeah, you're right on your inputs..I'll look deep into the best transformation through WOE in detail.. :)</p>
<p>Thanks Sandeep, Sorry to have missed your reply..Yeah, you're right on your inputs..I'll look deep into the best transformation through WOE in detail.. :)</p> I assume you use logistic reg…tag:www.analyticbridge.datasciencecentral.com,2012-03-24:2004291:Comment:1831832012-03-24T17:22:26.323ZSandeep Sunkarahttps://www.analyticbridge.datasciencecentral.com/profile/SandeepSunkara
<p>I assume you use logistic regression post applying WOE transformation(?)!</p>
<p></p>
<p>X: Independent Variable</p>
<p>Y: Binary Dependent Variable</p>
<p>T(.): Transformation function </p>
<p></p>
<p>The fundamental assumption in logistic regression is 'X' and 'Log-Odds of Y' are linearly related. But, in most of the real world problems you will not see this relation restricting you to start with the model. What you need to do then? Apply a best transformation 'T(.)' to your 'X' that makes…</p>
<p>I assume you use logistic regression post applying WOE transformation(?)!</p>
<p></p>
<p>X: Independent Variable</p>
<p>Y: Binary Dependent Variable</p>
<p>T(.): Transformation function </p>
<p></p>
<p>The fundamental assumption in logistic regression is 'X' and 'Log-Odds of Y' are linearly related. But, in most of the real world problems you will not see this relation restricting you to start with the model. What you need to do then? Apply a best transformation 'T(.)' to your 'X' that makes 'T(X)' and 'Log-Odds of Y' linearly related. With some mathematics done on WOE formula you will realize that 'WOE' is one of the best transformations that makes the above said assumption valid!</p>
<p></p>
<p>Another reason could be possibly, all the independent variables will be standardized by applying WOE transformation. The co-efficients (betas) you will get will be on the same scale (in fact unit free), they can then be directly comparable for relative inferences.</p>
<p></p>
<p>Hope this helps!</p>
<p>Sunkara!</p>