Konstantinos Chlouverakis's Posts - AnalyticBridge2021-01-19T15:35:50ZKonstantinos Chlouverakishttps://www.analyticbridge.datasciencecentral.com/profile/KonstantinosChlouverakishttps://storage.ning.com/topology/rest/1.0/file/get/2236857427?profile=original&width=48&height=48&crop=1%3A1https://www.analyticbridge.datasciencecentral.com/profiles/blog/feed?user=0to7yrxhg2woe&xn_auth=noQuestion on Regressiontag:www.analyticbridge.datasciencecentral.com,2014-12-20:2004291:BlogPost:3169712014-12-20T17:52:35.000ZKonstantinos Chlouverakishttps://www.analyticbridge.datasciencecentral.com/profile/KonstantinosChlouverakis
<p>Hi team,</p>
<p></p>
<p>I am facing a simple problem and trying to find the optimum solution:</p>
<p></p>
<p>Y(cont) = x1(cat) + x2(cat) +x3(cat) + x4(cat) + x5(cont)</p>
<p></p>
<p>Where: cat = categorical and cont = continuous.My categorical variables have 100 classes.</p>
<p></p>
<p>So my Y is cont and 4/5 Xs are categorical. What is the optimum approach? ANOVA? For ANOVA I think that would be true only when ALL of my Xs were categorical. If I simply apply a linear regression, then I…</p>
<p>Hi team,</p>
<p></p>
<p>I am facing a simple problem and trying to find the optimum solution:</p>
<p></p>
<p>Y(cont) = x1(cat) + x2(cat) +x3(cat) + x4(cat) + x5(cont)</p>
<p></p>
<p>Where: cat = categorical and cont = continuous.My categorical variables have 100 classes.</p>
<p></p>
<p>So my Y is cont and 4/5 Xs are categorical. What is the optimum approach? ANOVA? For ANOVA I think that would be true only when ALL of my Xs were categorical. If I simply apply a linear regression, then I would have 400 dummy Xs + 1 continuous.</p>
<p>I tried that in SAS and it gives me some results, but I am afraid if these are biased.</p>
<p></p>
<p>Thanks!</p>Binary Classification with no training settag:www.analyticbridge.datasciencecentral.com,2014-03-16:2004291:BlogPost:2911722014-03-16T23:43:59.000ZKonstantinos Chlouverakishttps://www.analyticbridge.datasciencecentral.com/profile/KonstantinosChlouverakis
<p>Hi guys,</p>
<p></p>
<p>I have this question. I have a dataset with unique IDs (people).</p>
<p>Each one has some attributes. I want to classify them to good and bad customers.</p>
<p>Since I donot have a training set (i.e. having for some IDs their score 0 or 1), how can I classify them to 2 groups?</p>
<p></p>
<p>I understand that regression (logistic for example) cannot take place since I donot have a dependent variable.</p>
<p>One solution could be clustering for example and have only 2…</p>
<p>Hi guys,</p>
<p></p>
<p>I have this question. I have a dataset with unique IDs (people).</p>
<p>Each one has some attributes. I want to classify them to good and bad customers.</p>
<p>Since I donot have a training set (i.e. having for some IDs their score 0 or 1), how can I classify them to 2 groups?</p>
<p></p>
<p>I understand that regression (logistic for example) cannot take place since I donot have a dependent variable.</p>
<p>One solution could be clustering for example and have only 2 clusters? (If I am lucky and these are well differentiated).</p>
<p></p>
<p>Thanks!</p>