"I have tried to use the WOE = ln(bad_distribution/good_distribution)
when the age variable
age band bads goods
19-25 2388 2019 8
26-30 1920 1716 24
31-35 1399 1377 53
36-40 1097 1157 73
41-45 934 1126 113
46-50 628 948 180
>50 527 876 209
The…"
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Sharath Dandamudi's Discussions
Reasons behind deterioration of r squared for an OLS model over a period of time
Hi!,I am working on a project which involves investigation of deterioration in R squared over a period of time for an OLS model. I am wondering how to approach this problem considering the fact that…Continue
Started Nov 16, 2011
Computation of Weight of Evidence when either the number of bads or goods in a class of a variable is 0
3 Replies
Hi All!I want to understand the ways in which Weight of Evidence (WoE) is computed or adjusted in the following scenarios: 1. When number of goods in a class of a variable is 02. When number of bads…Continue
Started this discussion. Last reply by kiran chapidi Dec 11, 2012.
Pros and cons of Dummy variable vs WoE approach for variables in Model building
4 Replies
Hi! I need inputs on the pros and cons of building a log-reg model using dummy variables instead of the Weight of evidence approach for categorical variables. Some of the cons that I can think of…Continue
Started this discussion. Last reply by Sandeep Sunkara Mar 24, 2012.
Books or Reference material for FRAUD & RISK ANALYTICS
Hi!, Can anyone suggest titles of books or reference material found on the web for FRAUD & RISK ANALYTICS? Regards,SharathContinue
Started Apr 5, 2011
Sharath Dandamudi's Page
Latest Activity
kiran chapidi replied to Sharath Dandamudi's discussion Computation of Weight of Evidence when either the number of bads or goods in a class of a variable is 0
Branko Mlikota replied to Sharath Dandamudi's discussion Computation of Weight of Evidence when either the number of bads or goods in a class of a variable is 0
RockyRambo replied to Sharath Dandamudi's discussion Data points for sensitivity and 1-specificity in ROC
"Hi Sharath,
Since sensitivity and (1- specificity) are determined using different cut off points from the confusion matrix, you can get both of them by varying the cutoff points..Let's say I have scores of 1 million observations in my model. If…"
Sharath Dandamudi posted a discussion
Reasons behind deterioration of r squared for an OLS model over a period of time
Hi!,I am working on a project which involves investigation of deterioration in R squared over a period of time for an OLS model. I am wondering how to approach this problem considering the fact that there are few dummy independent variables as well along with a few continuous ones. Do I have to check if there is any shift at an overall model level and characteristic level? If there are any other approaches that are adopted in the industry then please let me know. Any help on this would be…See More
Jozo Kovac replied to Sharath Dandamudi's discussion Computation of Weight of Evidence when either the number of bads or goods in a class of a variable is 0
"Exactly as you've written - it's undefined for some categories.
Such categories can't be used by logistic regression as well.
You have several options:
- discard attributes having such categories
- merge categories so none of them…"
Sharath Dandamudi posted a discussion
Computation of Weight of Evidence when either the number of bads or goods in a class of a variable is 0
Hi All!I want to understand the ways in which Weight of Evidence (WoE) is computed or adjusted in the following scenarios: 1. When number of goods in a class of a variable is 02. When number of bads in a class of a variable is 0 WoE = ln(distribution of goods/distributions of bads) Scenario 1: WoE=ln(0) ?? when number of goods in a class =0.Scenario 2: WoE=ln(distribution of goods/0)=ln(infinity) ?? when number of bads in a class = 0. Regards,Sharath See More
Jozo Kovac replied to Sharath Dandamudi's discussion Pros and cons of Dummy variable vs WoE approach for variables in Model building
"I understand well. It's about terminology.
WoE=Weight of Evidence is metrics and has own formula.
Dummy variable is binary flag created from categorical variable with more than 2 categories.
And again - simpler model is better. If…"
Sharath Dandamudi replied to Sharath Dandamudi's discussion Pros and cons of Dummy variable vs WoE approach for variables in Model building
"Hi Jozo,
Thanks for the reply. What I meant by WoE vs Dummy is say for eg. there is a categorical variable (independent variable, of course) with 4 levels-
Colour- Blue, Green, Red and White
The two ways I mentioned about is computing WoE for…"
Jozo Kovac replied to Sharath Dandamudi's discussion Pros and cons of Dummy variable vs WoE approach for variables in Model building
"First - you can compute WoE for both dummy and categorical variable, they aren't competitors.
Second - dummies lower degrees of freedom, produce simpler models and simpler is better according Occam's razor. And maybe also less sensitive to…"
Sharath Dandamudi's discussion was featured
Pros and cons of Dummy variable vs WoE approach for variables in Model building
Hi! I need inputs on the pros and cons of building a log-reg model using dummy variables instead of the Weight of evidence approach for categorical variables. Some of the cons that I can think of using Dummy variable approach are: 1. Overfitting2. Interpretation of output I know one of the things that needs to be looked at is the number of unique levels within a categorical variable. But, making reasonable assumptions, in a generic sense I would like to know if there are any pros and few other…See More
Sharath Dandamudi posted a discussion
Pros and cons of Dummy variable vs WoE approach for variables in Model buildning
Hi! I need inputs on the pros and cons of building a log-reg model using dummy variables instead of the Weight of evidence approach for categorical variables. Some of the cons that I can think of using Dummy variable approach are: 1. Overfitting2. Interpretation of output I know one of the things that needs to be looked at is the number of unique levels within a categorical variable. But, making reasonable assumptions, in a generic sense I would like to know if there are any pros and few other…See More
Daniel I. Shostak replied to Sharath Dandamudi's discussion Difference between Prediction and Forecasting
"HI Sharath:
I'm president of Strategic Affairs Forecasting LLC and am a futurist that has made very careful distinctions between prediction and forecast for many years. Here are the key elements in my opinion:
-For a number of…"
Miles Garnsey replied to Sharath Dandamudi's discussion Handling problem of Multi-collinearity
"I not 100% sure about logistic regression, but for normal regression you should keep whichever variable has the strongest correlation, because it accounts for the most variance. If you have four all accounting for the same variance, it'll only…"
Richard Boire replied to Sharath Dandamudi's discussion Handling problem of Multi-collinearity
"Sorry about that. pressed the post button too early. The more experienced approach as opposed to academic approach is as follows:
1)This is only a problem if the actual variable switches in sign in the multivariate routine from what you observe in…"
Sharath Dandamudi posted a discussion
Books or Reference material for FRAUD & RISK ANALYTICS
Hi!, Can anyone suggest titles of books or reference material found on the web for FRAUD & RISK ANALYTICS? Regards,SharathSee More
Mikael Bunicich replied to Sharath Dandamudi's discussion Difference between Prediction and Forecasting
"In book D. E. Catlin "estimation, control, and the discrete Kalman filter" the word prediction is used as:
hatx(k | j) for j=k is called the filtered estimate of x(k)
hatx(k | j) for j<k is called the predicted estimate of…"
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