Neural Network books

I was talking with a colleague today who is taking a business-oriented data mining course, and there was a list of neural network books recommended by the instructor. It was fascinating looking at the books in the list because I didn't know several of them. When I examined several of the recommended books on amazon.com, I found they contained what I would call "academic" treatments of neural networks. That means they covered all kinds of varieties of neural networks, including brain-state-in-a-box, Boltzmann machines, Hebbian networks, Adaline, ART1, ART2, and many more. Now I have nothing against learning about these techniques on the graduate school level, or even on the undergraduate level. But for practitioners, I see absolutely no advantage here because they aren't used in practice. Nearly always, when someone says they are building a "neural network" they mean a Multi-layered perceptron (MLP).

When I use neural networks in major software packages, such as IBM-SPSS Modeler, Statistica, Tibco Spotfire Miner, SAS Enterprise Miner, JMP, Affinium Predictive Insight, and I can go on... I am building MLPs, not ART3 models. So why teach professionals how these other algorithms work? I don't know.

Now neural network experts I'm sure will find times and places to build esoteric varieties of neural nets. But because of the way most practitioners actually build neural networks, I recommend sticking with the MLP, and understanding the vast numbers of options one has just with this algorithm. This is one reason I like the Christopher Bishop Neural Networks for Pattern Recognition. Check out the table of contents--I think these topics are more helpful to understand than learning more neural network algorithms.

Another option for spinning up on neural nets is the excellent SAS Neural Network FAQ which is old, but still a very clear introduction to the subject. Finally, for backpropagation, I also like the Richard Lippmann 1987 classic "An Introduction to Computing with Neural Nets (8MB here).

Salford to Launch New Integrated Data Mining Suite

Tomorrow night is the launch of SPM (Salford Predictive Miner). If you are in San Diego, give them a holler to let them know you are coming. See you there!

A/B Testing and the Need for Clear Business Objectives

The website http://videolectures.net/ contains a wealth of interesting lectures on a wide variety of topics, including data mining. I was reminded of one today by Ronny Kohavi entitled  "Practical Guide to Controlled Experiments on the Web: Listen to Your Customers not to the HiPPO" It's short (only 23 minutes) and filled with some very good common-sense principles.

First, it is a talk about the importance of A/B testing, or in other words, constructing experiments to learn customer behavior rather than having the experts make a best guess at how people will behave. He gives some good examples from Microsoft and the sometimes non-intuitive results from actual testing. A book he recommends is Breakthrough Business Results With MVT: A Fast, Cost-Free, Secret Weapon for Boosting Sales, Cutting Expenses, and Improving Any Business Process

The second part of the lecture I found particularly interesting is what Kohavi calls the Overall Evaluation Criterion (OEC), or what I usually call business objectives. He included the great Lewis Carroll quote, "If you don't know where you are going, any road will take you there." I find this a common problem as well: if we don't define a business objective that truly measures the impact of the predictive models we build, we have no way of determining if they are effective or not.  This objective must be tied to the business itself. For example, Kohavi argues for using Customer Lifetime Value (CLV) rather than click-through rates as they are more tied to the bottom line.

I would add that it can be useful to have two objectives that are measurable, especially if two objectives better measure the value. For example, in collections risk models, the age of the debt and the amount of the debt are both important components to risk. These are difficult to put into a single number in general, so the two-dimensional risk score can be helpful operationally.

Embedded Analytics and Business Rules: The Holy Grail?

Tomorrow (Thursday) at 3pm EDT I'll be on DM Radio for the broadcast "Embedded Analytics and Business Rules: The Holy Grail?".  I'm not sure what the other guests are going to talk about, but my comments will resemble the talk I gave at Predictive Analytics World in February 2010 in the talk Rules Rule: Inductive Business-Rule Discovery in Text Mining. In this help-desk case study, we used decision trees to cherry pick interesting rules, converted them to SQL, and deployed them in a rule system that was applied transactionally, online. I emphasized the text mining portion at PAW, but the methodology was independent of that. In 2002-2003, researchers and I at the IRS applied same kind of approach to rule discovery in selecting returns for audit: use trees to find interesting rules.

The reason we liked the approach was that it was a fast way to overcome two problems. First, decision trees only find the best solution to a problem (according to its measure of "good"). To obtain a richer set of terminal nodes, one can build ensembles of trees, but then one loses the interpretation. On the other hand, one can build association rules, but then you are left with perhaps thousands to tens of thousands of rules that have to be pruned back to get the gist of the key ideas. Many of the rules will be redundant (some completely identical in which records are "hit" by the rule), and it's easy to become lost in the sheer number of rules.

For the Fortune 500 company, we used CART with the battery option to generate a sequence of trees (we iterated on "priors" and misclassification costs, and I think some more options as well to generate variety), and took only those terminal nodes that had sufficiently high classification accuracy. I think we could have used their hotspot analysis for this too, but I wasn't sufficiently well-versed in it at that time.

If you can't join in on the radio broadcast, you can always download the mp3 later.

PAKDD-10 Data Mining Competition Winner: Ensembles Again!

The PAKDD-10 Data Mining Competition results are in, and ensembles occupied the top 4 positions, and I think the top 5. The winner used Stochastic Gradient Boosting and Random Forests in Statistica, second place a combination of logistic regression and Stochastic Gradient Boosting (and Salford Systems CART for some feature extraction). Interestingly to me, the 5th place finisher used WEKA, an open source software tool.

The problem was credit risk with biased data for building the models, a good way to do the competition because this is the problem we usually face anyway: data was collected based on historic interactions with the company, biased by the approaches the company has used in the past rather than having a pure random sample to build models. Model performance was judged based on  Area under the Curve (AUC), with the KS distance as the tie breaker (it's not everyday I hear folks pull out the KS distance!).

One submission in particular commented on the difference between how algorithms build models and the metric used to evaluate them. CART uses the Gini Index, Logistic regression the log-odds, Neural Networks minimize mean squared error (usually), none of which directly maximize AUC. But this topic is worthy of another post.

The Trimmed Mean has Intuitive Appeal

I was listening to Colin Cowherd of ESPN radio this morning and he made a very interesting observation that we data miners know, or at least should know and make good use of. The context was evaluating teams and programs: are they dynasties or built off of one great player or coach. Lakers? dynasty. Celtics? dynasty. Bulls? without Jordan, they have been a mediocre franchise. The Lakers without Magic are still a dynasty. The Celtics without Bird are still a dynasty.

So his rule of thumb that he applied to college football programs was this: remove the best coach and the worst coach, and then assess the program. If they are still a great program, they are truly a dynasty.

This is the trimmed (truncated) mean idea that he was applying intuitively but is quite valuable in practice. When we assess customer lifetime value, if a small percentage of the customers generate 95% of the profits, examining those outliers or the long tail while valuable does not get at the general trend. When I was analyzing IRS corporate tax returns, the correlation between two line items (that I won't identify here!) was more than 90% over the 30K+ returns. But when we removed the largest 50 corporations, the correlation between these line items dropped to under 30%. Why? Because the tail drove the relationship; the overall trend didn't apply to the entire population. It is easy to be fooled by summary statistics for this reason: they assume characteristics about the data that may not be true.

This all gets back to nonlinearity in the data: if outliers behave differently than the general population, assess them based on the truncated populations. If outliers exist in your data, get the gist from the trimmed mean or median to reduce the bias from the outliers. We know this intuitively, but sometimes we forget to do it and make misleading inferences.

[UPDATE] I neglected to reference a former post that shows the problem of outliers in computing correlation coefficients: Beware of Outliers in Computing Correlations.

Upcoming DMRadio Interview: Analytics and Business Rules

On June 3rd, a week from this Thursday, I'll be participating in my third DMRadio interview, this time on business rules (the first two were related to text mining, including this one last year). I always have found these interviews enjoyable to do. I'll probably be discussing an inductive rule discovery process I participated in with a Fortune 500 company (and described at last February's Predictive Analytics World Conference in San Francisco).

Even if you can't be there "live", you can download the interview later.

Data Mining as a Top Career

More good news for data miners: http://www.signonsandiego.com/news/2010/may/19/hot-career-trends-for-college-grads-listed-in/

Data mining. The field involves extracting specific information or patterns from large databases. Career prospects are available in areas including advertising technology, scientific research and law enforcement.

I think they got it right: data mining (and it's siblings Predictive Analytics and Business Analytics) are growing in their appeal. But more importantly, I see organizations believing they can do it.

Of course time will tell. One sign will be how many more resumes (unsolicited) I get!

web analytics and predictive analytics: comments from emetrics

I just got back from the latest (and my first) eMetrics conference in San Jose, CA last week, and was very impressed by the practical nature of the conference. It was also a quite different experience for me to be in a setting where I knew very very few people there. I was there to co-present with Angel Morales "Behavioral Driven Marketing Attribution". Angel and I are co-founders of SmarterRemarketer, a new web analytics company, and this solution we described is just one nut we are trying to crack in the industry.

This post though is related to the overlap between web analytics and predictive analytics: very little right now. It really is a different world, and for many I spoke with, the mere mention of "predictive analytics" resulted in one of those unknowing looks back at me. In fairness, much that was spoken to me resulted in the same look!

One such topic was that of "use cases", a term used over and over in talks, but one that I don't encounter in the data mining world. We describe "case studies", but a "use case" is a smaller and more specific example of something interesting or unusual in how individuals or groups of individuals interact with web sites (I hope I got that right). The key though is that this is a thread of usage. In data mining, it is more typical that predictive models are built, and then to understand why the models are the way they are, one might trace through some of the more interesting branches of a tree or unusual variable combinations in something similar to this "use case" idea.

First, what to commend... The analyses I saw were quite good: customer segmentation, A/B testing, web page layout, some attribution, etc. There was a great keynote by Joe Megibow of Expedia describing how Expedia's entire web presence has changed in the past year. One of my favorite bloggers, Kevin Hillstrom of MineThatData fame gave a presentation praising the power of conditional probabilities (very nice!).  Lastly, there was one more keynote by someone I had never heard of (not to my credit), but is obviously a great communicator and is well-known in the web analytics world, Avinash Kaushik. One idea I liked very much from his keynote was the long tail: the tail of the distribution of keywords that navigates to his website contains many times more visits than his top 10. In the data mining world, of course, this would push us to characterize these sparsely populated items differently so they produce more influence in any predictive models. Lots to think about.

But I digress. The lack of data mining and predictive analytics at this conference begs (at least from me) the question: why not? They are swimming in data, have important business questions that need to be solved, and clearly not all of these are being solved well enough. That will be the subject of my next post.

Rexer Analytics Data Mining Survey

Calling all data miners! I encourage all to fill out the survey--it is the most complete survey of the data mining world that I am aware of. Use the link and code below, and stay tuned to see the results later in the year.

Survey Link:  www.RexerAnalytics.com/Data-Miner-Survey-2010-Intro2.html Access Code:  RS2458 The full description sent by Karl Rexer is below: Rexer Analytics, a data mining consulting firm, is conducting our fourth annual survey of the analytic behaviors, views and preferences of data mining professionals.  We would greatly appreciate it if you would: 1)       Participate in this survey, and 2)       Tell other data miners about the survey (forward this email to them). Thank you.  Forwarding the survey to others is invaluable for our “snowball sample methodology”.  It helps the survey reach a wide and diverse group of data miners.   Thank you also to everyone who participated in previous Data Miner Surveys, and especially to the people who provided suggestions for new questions and other survey modifications.  This year’s survey incorporates many ideas from survey participants. Your responses are completely confidential: no information you provide on the survey will be shared with anyone outside of Rexer Analytics.  All reporting of the survey findings will be done in the aggregate, and no findings will be written in such a way as to identify any of the participants.  This research is not being conducted for any third party, but is solely for the purpose of Rexer Analytics to disseminate the findings throughout the data mining community via publication, conference presentations, and personal contact.  If you would like a summary of last year’s or this year’s findings emailed to you, there will be a place at the end of the survey to leave your email address.  You can also email us directly (DataMinerSurvey@RexerAnalytics.com) if you have any questions about this research or to request research summaries. To participate, please click on the link below and enter the access code in the space provided.  The survey should take approximately 20 minutes to complete.  Anyone who has had this email forwarded to them should use the access code in the forwarded email. Survey Link:  www.RexerAnalytics.com/Data-Miner-Survey-2010-Intro2.html Access Code:  RS2458 Thank you for your time.  We hope the results from this survey provide useful information to the data mining community.