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Mike Laracy replied to Panchanana M.R.L.N.'s discussion PAY-NOPAY Model and Payment Projection Scorecard

"To combine the information from the two models, you can simply multiply the two model scores, which will give you an expected value. You've got two populations of customers (payers and non-payers). In your first model, you are coming up with a…"

Nov 22, 2010

Mike Laracy replied to Rahul V's discussion Modeling when only event data is available in the group Data Mining

"I agree with Jozo. If historically your response rate is relatively low, you can get away with creating your dataset using the known responders (and they are obviously the 'ones' in terms of the Y, and using everyone else as the zeros. The…"

Nov 24, 2009

Mike Laracy replied to Yi-Chun Tsai's discussion credit score model

"Yi-Chun, It's less a matter of what technique to use and more a matter of what data you have available to build the model. What historical data do you have on your customers (i.e. -credit/default history, demographic, financial and credit data)."

Nov 4, 2009

Mike Laracy replied to Vincent Granville's discussion Bias in car accident studies

"Great points Biswajit. The more information, the better. The only problem with some of the variables (wearing shoes etc...), is that you might have that information from accident reports in some cases, but even if you had that information for a…"

Sep 16, 2009

Mike Laracy replied to Vincent Granville's discussion Bias in car accident studies

"I agree with you Steffen that there are still cars that are more safe and less safe than other cars due to their technical capabilities. Vincent was trying to explore whether there might be other influencing factors, aside from the car's…"

Sep 16, 2009

Mike Laracy replied to DataLLigence's discussion Test for Difference in Proportions - T Test? Proc GLM?

"Good points Matt. If a difference in a statistic between two groups is found to be “statistically significant’ it simply means that based on sample size, variation, and the value of the measured statistic, the difference you have seen is…"

Sep 9, 2009

1	Map of Data Science University Programs
2	Use PRESS, not R squared to judge predictive power of regression
3	Why is it so hard to count this way?
4	Text mining: alternative to Wordflex?
5	Audience expansion
6	How to develop churn prediction model for telecom company?
7	Strategy for building a “good” predictive model
8	The End of Theory: The Data Deluge Makes the Scientific Method Obsolete \| Wired
9	Identifying the number of clusters: finally a solution
10	Can we automate data mining?

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Top Content

Map of Data Science University Programs

Use PRESS, not R squared to judge predictive power of regression

Why is it so hard to count this way?

Text mining: alternative to Wordflex?

Audience expansion

How to develop churn prediction model for telecom company?

Strategy for building a “good” predictive model

The End of Theory: The Data Deluge Makes the Scientific Method Obsolete | Wired

Identifying the number of clusters: finally a solution

Can we automate data mining?

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