Yesterday, I had the pleasure of being on a panel for a local TDWI event here in New York focused on advanced analytics (thank you Jon Deutsch). Mark Madsen of Third Nature gave an interesting, if rapid-fire, overview of data mining technologies. Of course, I was excited to see that Mark included Data Analysis Using SQL and Excel as one of the first steps in getting started in data mining -- even before meeting me. Besides myself, the panel included my dear friend Anne Milley from SAS, Ali Pasha from Teradata, and a gentleman from Information Builders whose name I missed.
I found one of the questions from the audience to be quite interesting. The person was from the IT department of a large media corporation. He has two analysis groups, one in Los Angeles that uses SPSS and the other in New York that uses SAS. His goal, of course, is to reduce costs. He prefers to have one vendor. And, undoubtedly, the groups are looking for servers to run their software.
This is a typical IT-type question, particularly in these days of reduced budgets. I am more used to encountering such problems in the charged atmosphere of a client. The more relaxed atmosphere of a TDWI meeting perhaps gives a different perspective.
The groups are doing the same thing from the perspective of an IT director. Diving in a bit futher, the two groups do very different things -- at least from my perspective. Of course, both are using software running on computers to analyze data. The group in Los Angeles is using SPSS to analyze survey data. The group in New York is doing modeling using SAS. I should mention that I don't know anyone in the groups, and only have the cursory information provided at the TDWI conference.
Conflict Alert! Neither group wants to change and both are going to put up a big fight. SPSS has a stronghold in the market for analyzing survey data, with specialized routines and procedures to handle this data. (SAS probably has equivalent functionality, but many people who analyze survey data gravitate to SPSS.) Similarly, the SAS programmers in New York are not going to take kindly to switching to SPSS, even if offers the same functionality.
Each group has the skills and software that they need. Each group has legacy code and methods, that are likely tied to their tools. The company in question is not a 20-person start-up. It is a multinational corporation. Although the IT department might see standarizing a tool as beneficial, in actual fact, the two groups are doing different things and the costs of switching are quite high -- and might involve losing skilled people.
This issue brings up the question of what do we want to standardize on. The value of advanced analytics comes in two forms. The first is the creative process of identifying new and interesting phenomena. The second is the communication process of spreading the information where it is needed.
Although people may not think of nerds as being creative, really, we are. It is important to realize that imposing standards or limiting resources may limit creativity, and hence the quality of the results. This does not mean that cost control is unnecessary. Instead, it means that there are intangible costs that may not show up in a standard cost-benefit analysis.
On the other hand, communicating results through an organization is an area where standards are quite useful. Sometimes the results might be captured as a simple email going to the right person. Other times, the communication must go to a broader audience. Whether byy setting up an internal Wiki, updating model scores in a database, or loading a BI tool, having standards is important in this case. Many people are going to be involved, and these people should not have to learn special tools for one-off analyses -- so, if you have standardized on a BI tool, make the resources available to put in new results. And, from the perspective of the analysts, having standard methods of communicating results simplifies the process of transforming smart analyses into business value.
Showing posts with label user question. Show all posts
Showing posts with label user question. Show all posts
Thursday, September 10, 2009
Saturday, April 25, 2009
When There Is Not Enough Data
I have a dataset where the target (continuous variable) variable that has to be estimated. However, in the given dataset, values for target are preset only for 2% while rest of 98% do not have values. The 98% are empty values. I need to score a dataset and give values for the target for all 2500 records. Can I use the 2% and replicate it several times and use that dataset to build a model? The ASE is too high if I use the 2% data alone. Any suggestions how to handle it, please?
Sneha,
The short answer to your question is "Yes, you can replicate the 2% and use it to build a model." BUT DO NOT DO THIS! Just because a tool or technique is possible to implement does not mean that it is a good idea. Replicating observations "confuses" models, often by making the model appear overconfident in its results.
Given the way that ASE (average squared error) is calculated, I don't think that replicating data is going to change the value. We can imagine adding a weight or frequency on each observation instead of replicating them. When the weights are all the same, they cancel out in the ASE formula.
What does change is confidence in the model. So, if you are doing a regression and looking at the regression coefficients, each has a confidence interval. By replicating the data, the resulting model would have smaller confidence intervals. However, these are false, because the replicated data has no more information than the original data.
The problem that you are facing is that the modeling technique you are using is simply not powerful enough to represent the 50 observations that you have. Perhaps a different modeling technique would work better, although you are working with a small amount of data. For instance, perhaps some sort of nearest neighbor approach would work well and be easy to implement.
Thanks,
Sneha
Sneha,
The short answer to your question is "Yes, you can replicate the 2% and use it to build a model." BUT DO NOT DO THIS! Just because a tool or technique is possible to implement does not mean that it is a good idea. Replicating observations "confuses" models, often by making the model appear overconfident in its results.
Given the way that ASE (average squared error) is calculated, I don't think that replicating data is going to change the value. We can imagine adding a weight or frequency on each observation instead of replicating them. When the weights are all the same, they cancel out in the ASE formula.
What does change is confidence in the model. So, if you are doing a regression and looking at the regression coefficients, each has a confidence interval. By replicating the data, the resulting model would have smaller confidence intervals. However, these are false, because the replicated data has no more information than the original data.
The problem that you are facing is that the modeling technique you are using is simply not powerful enough to represent the 50 observations that you have. Perhaps a different modeling technique would work better, although you are working with a small amount of data. For instance, perhaps some sort of nearest neighbor approach would work well and be easy to implement.
You do not say why you are using ASE (average squared error) as the preferred measure of model fitness. I can speculate that you are trying to predict a number, perhaps using a regression. One challenge is that the numbers being predicted often fall into a particular range (such as positive numbers for dollar values or ranging between 0 and 1 for a percentage). However, regressions produce numbers that run the gamut of values. In this case, transforming the target variable can sometimes improve results.
In our class on data mining (Data Mining Techniques: Theory and Practice), Michael and I introduce the idea of oversamping rare data using weights in order to get a balanced model set. For instance, if you were predicting whether someone was in the 2% group, you might give each of them a weight of 49 and all the unknowns a weight of 1. The result would be a balanced model set. However, we strongly advise that the maximum weight be 1. So, the weights would be 1/49 for the common cases and 1 for the rare ones. For regressions, this is important because it prevents any coefficients from having too-narrow confidence intervals.
In our class on data mining (Data Mining Techniques: Theory and Practice), Michael and I introduce the idea of oversamping rare data using weights in order to get a balanced model set. For instance, if you were predicting whether someone was in the 2% group, you might give each of them a weight of 49 and all the unknowns a weight of 1. The result would be a balanced model set. However, we strongly advise that the maximum weight be 1. So, the weights would be 1/49 for the common cases and 1 for the rare ones. For regressions, this is important because it prevents any coefficients from having too-narrow confidence intervals.
Thursday, June 5, 2008
Qualifications for Studying Data Mining
A recent question . . .
I am hoping to begin my masters degree in Data Mining. I have come from a Software Development primary degree. I am a bit worried over the math involved in Data Mining.Could you tell me, do I need to have a strong mathematical aptitude to produce a good Thesis on Data Mining?
First, I think a software development background is a good foundation for data mining. Data mining is as much about data (and hence computers and databases) as it is about analysis (and hence statistics, probability, and math).
Michael and I are not academics so we cannot speak to the thesis requirements for a particular data mining program. Both of us majored in mathematics (many years ago) and then worked as software engineers. We do have some knowledge of both fields, and the combination provided a good foundation for our data mining work.
To be successful in data mining, you do need some familiarity with math, particularly applied math -- things like practical applications of probability, algebra, the ability to solve word problems, and the ability to use spreadsheets. Unlike theoretical statistics, the purpose of data mining is not to generate rigorous proofs of various theorems; the purpose is to find useful patterns in data, to validate hypotheses, to set up marketing tests. We need to know when patterns are unexpected, and when patterns are expected.
This is a good place to add a plug for my book Data Analysis Using SQL and Excel, which has two or three chapters devoted to practical statistics in the context of data analysis.
In short, if you are math-phobic, then you might want to reconsider data mining. If your challenges in math are solving complex integrals, then you don't have much to worry about.
--gordon
I am hoping to begin my masters degree in Data Mining. I have come from a Software Development primary degree. I am a bit worried over the math involved in Data Mining.Could you tell me, do I need to have a strong mathematical aptitude to produce a good Thesis on Data Mining?
First, I think a software development background is a good foundation for data mining. Data mining is as much about data (and hence computers and databases) as it is about analysis (and hence statistics, probability, and math).
Michael and I are not academics so we cannot speak to the thesis requirements for a particular data mining program. Both of us majored in mathematics (many years ago) and then worked as software engineers. We do have some knowledge of both fields, and the combination provided a good foundation for our data mining work.
To be successful in data mining, you do need some familiarity with math, particularly applied math -- things like practical applications of probability, algebra, the ability to solve word problems, and the ability to use spreadsheets. Unlike theoretical statistics, the purpose of data mining is not to generate rigorous proofs of various theorems; the purpose is to find useful patterns in data, to validate hypotheses, to set up marketing tests. We need to know when patterns are unexpected, and when patterns are expected.
This is a good place to add a plug for my book Data Analysis Using SQL and Excel, which has two or three chapters devoted to practical statistics in the context of data analysis.
In short, if you are math-phobic, then you might want to reconsider data mining. If your challenges in math are solving complex integrals, then you don't have much to worry about.
--gordon
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