EDIT: ***   Based on some of the answers, I guess its better to keep race in normal format for the modeling     Race - Target (originally was 6 levels but is now converted to Binary 0,1)  The original Race column is known as 'Race_Label'.  Mental_Illness - Target Binary   Firstly I am using both as my target variables and building separate predictive models to analyze their relationship with input variables. The other input variables I think right is irrelevant to my question so I am only talking about both my targets and what I want to try and do with them.    My first predictive model to build is using my 'mental illness as a target to create a decision tree and regression model, it includes the use of the race variable as an input as I have to analyze its relation to mental illness. My question is that should I use my race variable as the converted binary variable for the model or use it as it is in its original format?    This is how I am trying to choose the best model. One model is using the converted race variable as an input while the other model is using race as a nominal (6 level) input variable. I am trying to see the difference but I am getting confused as I feel like I am wasting time trying to see what option would be better.    My data partition is set at 70/30.   And below are the outputs of one of the types of decision tree model (Splitting criteria is set at Gini with Decision/Misclassification Rate as the assessment measure or pruning measure) with first the converted race variable as input.   and the second picture shows the original race variable being used as input.   I hope I am making sense. Thank in advance for your help everyone.    ... View more

Hello,   So I have done a lot of research on regression modelling and according to many articles, some people have done dummy coding or group by rare levels to transform their categorical variables in SAS Enterprise Miner however some have allowed their model to run without transforming their categorical variables.   My question is that is that really necessary, for example, one of my categorical variables is Armed Category, this is something I preprocessed on my own and condensed from another column known as Armed. The armed category consists of approximately 10-11 levels and has 'Blunt Weapon', 'Gun', 'Other Weapon' etc.    Another variable is Region which has 5 levels.   I need to understand why is it so important to transform these? Can't we simply leave them as is and let the regression node run and produce the results? I know for certain interval variables we need to possibly log them and my only one is Age, which I am also a little confused about whether I should go ahead and bin it or leave it as is.    I have already removed all missing values etc.  ... View more

Hi,   I've gone through a few articles and haven't found proper information on how to analyze the chi-square in SAS EM. Here are my results     Can someone please give a detailed explanation of how this would be analyzed, I have the gist of it but would like to understand this more! ... View more

Hi Everyone,  The past few days I have went through a lot of questions and answers to help me out and it greatly did. But I would like to ask more questions   My target variables are Race and Mental Illness.    The dataset I've merged with certain factors such as county and preprocessed some information such as region, day, month and year in SAS Studio.   At the moment in my SAS Eminer I've made Decision Tree modeling each target variable and I need one more model. The regression model for M.I comes out fine, (I haven't analyzed it yet however it's producing properly) however when I produce one for Race, it looks weird and just doesn't look right and also because I have went through countless search to find out if there was a way to model Race using logistic regression but it seems impossible unless I have 2 levels or 3.   Can someone please suggest example of a model I may create in SAS EM which can work for both target variables. Please and thank you! ... View more