Data Preparation: Know Your Records!

Data preparation in data mining and predictive analytics (dare I also say Data Science?) rightfully focuses on how the fields in ones data should be represented so that modeling algorithms either will work properly or at least won't be misled by the data. These data preprocessing steps may involve filling missing values, reigning in the effects of outliers, transforming fields so they better comply with algorithm assumptions, binning, and much more.

In recent weeks I've been reminded how important it is to know your records. I've heard this described in many ways, four of which are:

the unit of analysis

the level of aggregation

what a record represents

unique description of a record

For example, does each record represent a customer? If so, over their entire history or over a time period of interest? In web analytics, the time period of interest may be a single session, which if it is true, means that an individual customer may be in the modeling data multiple times as if each visit or session is an independent event.

Where this especially matters is when disparate data sources are combined. If one is joining a table of customerID/Session data with another table with each record representing a customerID, there's no problem. But if the second table represents customerID/store visit data, there will obviously be a many-to-many join resulting in a big mess.

This is probably obvious to most readers of this blog. What isn't always obvious is when our assumptions about the data result in unexpected results. What if we expect the unit of analysis to be customerID/Session but there are duplicates in the data? Or what if we had assumed customerID/Session data but it was in actuality customerID/Day data (where ones customers typically have one session per day, but could have a dozen)?

The answer is just like we need to perform a data audit to identify potential problems with fields in the data, we need to perform record audits to uncover unexpected record-level anomalies. We've all had those data sources where the DBA swears up and down that there are no dups in the data, but when we group by customerID/Session, we find 1000 dups.

So before the joins and after joins, we need to do those group by operations to find examples with unexpected numbers of matches.

In conclusion: know what your records are supposed to represent, and verify verify verify. Otherwise, your models (who have no common sense) will exploit these issues in undesirable ways!

Budgeting Time on a Modeling Project

Within the time allotted for any empirical modeling project, the analyst must decide how to allocate time for various aspects of the process.  As is the case with any finite resource, more time spent on this means less time spent on that.  I suspect that many modelers enjoy the actual modeling part of the job most.  It is easy to try "one more" algorithm: Already tried logistic regression and a neural network?  Try CART next.

Of course, more time spent on the modeling part of this means less time spent on other things.  An important consideration for optimizing model performance, then, is: Which tasks deserve more time, and which less?

Experimenting with modeling algorithms at the end of a project will no doubt produce some improvements, and it is not argued here that such efforts be dropped.  However, work done earlier in the project establishes an upper limit on model performance.  I suggest emphasizing data clean-up (especially missing value imputation) and creative design of new features (ratios of raw features, etc.) as being much more likely to make the model's job easier and produce better performance.

Consider how difficult it is for a simple 2-input model to discern "healthy" versus "unhealthy" when provided the input variables height and weight alone.  Such a model must establish a dividing line between healthy and unhealthy weights separately for each height.  When the analyst uses instead the ratio of weight to height, this becomes much simpler.  Note that the commonly used BMI (body mass index) is slightly more complicated than this, and would likely perform even better.  Crossing categorical variables is another way to simplify the problem for the model.  Though we deal with a process we call "machine learning", is is a pragmatic matter to make the job as easy as possible for the machine.

The same is true for handling missing values.  Simple global substitution using the non-missing mean or median is a start, but think about the spike that creates in the variable's distribution.  Doing this over multiple variables creates a number of strange artifacts in the multivariate distribution.  Spending the time and energy to fill in those missing values in a smarter way (possibly by building a small model) cleans up the data dramatically for the downstream modeling process.

What do we call what we do?

I've called myself a data miner for about 15 years, and the field I was a part of as Data Mining (DM). Before then, I referred to what I did as "Pattern Recognition", "Machine Learning", "Statistical Modeling", or "Statistical Learning". In recent years, I've called what I do Predictive Analytics (PA) more often and even co-titled my blog with both Data Mining and Predictive Analytics. That stated, I don't have a good noun to go along with PA. A "predictive analytist" (as if I myself were a "predictor")? A "predictive analyzer"? I often call someone who does PA a Predictive Analytics Professional. But the according to google, the trending on data mining is down. Pattern recognition? Down. Machine Learning? Flat or slightly up. Only Predictive Analytics and it's closely-related sibling, Business Analytics, are up. Even the much-touted Data Science has been relatively flat, though has been spiking Q4 the past few years.

Data Mining

Pattern Recognition

Machine Learning

Predictive Analytics

Business Analytics

The big winner? Big Data of course! It has exploded this year. Will that trend continue? It's hard to believe it will continue, but this wave has grown and it seems that every conference related to analytics or databases is touting "big data".

Big Data

 

Data Science

I have no plans of calling what I do "big data" or "data science". The former term will pass when data gets bigger than big data. The latter may or may not stick, but seems to resonate more with theoreticians and leading-edge types than with practitioners. For now, I'll continue to call myself a data miner and what I do predictive analytics or data mining.

Choose Your Target Carefully

Every so often, an article or survey will appear stressing the importance of data preparation as an early step in the process of data mining.  One often-overlooked part of data preparation is to clearly define the problem, and, in particular, the target variable.  Often, a nominal definition of the target variable is given.

As an example, a common problem in banking is to predict future balances of a loan customer.  The current balance is a matter of record and a host of explanatory variables (previous payment history, delinquency history, etc.) are available for model construction.  It is easy to move forward with such a project without considering carefully whether the raw target variable is the best choice for the model to approximate.  It may be, for instance, that it is easier to predict the logarithm of balance, due to a strongly skewed distribution.  Or, it might be that it is easier to predict the ratio of future balances to the current balance.  These two alternatives result in models whose output are easily transformed back into the original terms (by exponentiation or multiplication by the current balance, respectively).  More sophisticated targets may be designed to stabilize other aspects of the behavior being studied, and certain other loose ends may be cleaned up as well, for instance when the minimum or maximum target values are constrained.

When considering various possible targets, it helps to keep in mind that the idea is to stabilize behavior, so that as many observations as possible align in the solution space.  If retail sales include a regular variation, such as by day of the week or month of the year, then that might be a good candidate for normalization: Possibly we want to model retail sales divided by the average for that day of the week, or retail sales divided by a trailing average for that day of the week for the past 4 weeks.  Some problems lend themselves to decomposition, such as profit being modeled by predicting revenue and cost separately.  One challenge to using multiple models in series this way is that their (presumably independent) errors will compound.

Experience indicates that it is difficult in practice to tell which technique will work best in any given situation without experimenting, but performance gains are potentially quite high for making this sort of effort.

The Data is Free and Computing is Cheap, but Imagination is Dear

Recently published research, What Makes Paris Look like Paris?, attempts to classify images of street scenes according to their city of origin.  This is a fairly typical supervised machine learning project, but the source of the data is of interest.  The authors obtained a large number of Google Street View images, along with the names of the cities they came from.  Increasingly, large volumes of interesting data are being made available via the Internet, free of charge or at little cost.  Indeed, I published an article about classifying individual pixels within images as "foliage" or "not foliage", using information I obtained using on-line searches for things like "grass", "leaves", "forest" and so forth.

A bewildering array of data have been put on the Internet.  Much of this data is what you'd expect: financial quotes, government statistics, weather measurements and the like- large tables of numeric information.  However, there is a great deal of other information: 24/7 Web cam feeds which are live for years, news reports, social media spew and so on.  Additionally, much of the data for which people once charged serious bucks is now free or rather inexpensive.  Already, many firms augment the data they've paid for with free databases on the Web.  An enormous opportunity is opening up for creative data miners to consume and profit from large, often non-traditional, non-numeric data which are freely available to all, but (so far) creatively analyzed by few.

Predicting Crime

Applying inferential statistics to criminology is not new, but it appears that the market has been maturing.  See, for instance, a recent article, "Police using ‘predictive analytics’ to prevent crimes before they happen", published by Agence France-Presse on The Raw Story (Jul-29-2012).

Setting aside obvious civil liberties questions, consider the application of this technology.  My suspicion is that targeting police efforts by geographic locale and day-of-week/time-of-day using this approach will decrease the overall level of crime, but by how much is not clear. This is typical of problems faced by businesses: It is not enough to predict what we already know, nor is it enough to trot out glowing but artificial technical measures of performance.  Knowledge that real improvement has occurred requires more.  For instance, at least some effect of police effort on the street does not decrease crime, but merely moves it to new locations.

Were I mayor of a small town approached by the vendor of such a solution, I'd want to see some sort of experimental design which made apples-to-apples comparison between our best estimates of what happens with the new tool, and what happens without it.  Only once this firm measure of benefit has been obtained could one reasonably weigh it against the financial and political costs.

Where it began: John Elder and Dean Abbott at Barron Associates

I came across this photo today and couldn't resist. John Elder and I worked at the company Barron Associates, Inc. (BAI) in Charlottesville, VA in the 80s (John was employee #1). Hopefully you can identify John in the back right and me in the front right, though it will take some good pattern matching to do so!

The Founder and President of the company was Roger Barron. Both John and I were introduced to statistical learning methods at BAI and of course went on to careers in the field now known as Data Mining or Predictive Analytics (among other things). I write about my experience with BAI in the forthcoming book Journeys to Data Mining: Experiences from 15 Renowned Researchers, Ed. by Dr Mohamed Medhat Gaber, published by Springer. Authors in the book include John (thanks to John for recommending my inclusion in the book), Gregory Piatetsky-Shapiro, Mohammed J. Zaki, and of course several others.

The photo appeared as I was searching for descriptions of our field back in the 80s and was looking in particular for the Barron and Barron paper "Statistical Learning Networks: A Unifying View", where "Statistical Learning Networks" was the phrase of interesting, along with "Models", "Classifiers", and "Neural Networks". We used to refer to the field as "Pattern Recognition" and "Artificial Intelligence". It's interesting to note that pattern recognition on Wikipedia contains a list of "See Also" terms that includes the more modern terms such as data mining and predictive analytics.

I will post within a couple days on the pattern recognition terms of the day and how they are changing.

Predictive Analytics World Had the Target Story First

The New York Times Magazine article "How Companies Learn Your Secrets" by Charles Duhigg with the key descriptions of Target, pregnancy, predictive analytics (blogged on here and here) certainly generated a lot of buzz; if you are unable to see the NYTimes Magazine article, the Forbes summary is a good summary. However, few know that Eric Siegel booked Andy Pole for the October 2010 Predictive Analytics World conference as a keynote speaker. The full video of that talk is here.

In this talk, Mr. Pole discussed how Target was using Predictive Analytics including descriptions of using potential value models, coupon models, and...yes...predicting when a woman is due (if you aren't the patient type, it is at about 34:30 in the video). These models were very profitable at Target, adding an additional 30% to the number of woman suspected or known to be pregnant over those already known to be (via self-disclosure or baby registries). The fact that this went on for over a year after the Predictive Analytics World talk and before the fallout tells me that it didn't cause significant problems for Target prior to the attention brought to the subject related to the NYTimes article.

After watching the talk, what struck me most was that Target was applying a true "360 deg" customer view by linking guests through store visits, web, email, and mobile interactions. In addition, close attention was paid to linking the interactions so that coupons made sense: they didn't print coupons to those who had just purchased items they score high for couponing, and they identify which mediums don't generate responses and stop using those channels.

I suspect what Target is doing is no different than most retailers, but this talk was an interesting glimpse into how much they value the different channels and try to find ways to provide customers with what they are most interested in, and suppress what they are not interested in.

Another Wisdom of Crowds Prediction Win at eMetrics / Predictive Analytics World

This past week at Predictive Analytics World / Toronto (PAW) has been a great time for connecting with thought leaders and practitioners in the field. Sometimes there are unexpected pleasures as well, which is certainly the case this time. One of the exhibitors for the eMetrics conference, co-locating with PAW at the venue, was Unilytics, a web analytics company. At their booth there was a cylindrical container filled with crumpled dollar bills with a sign soliciting predictions of how many dollar bills were in the container (the winner getting all the dollars). After watching the announcement of the winner, who guessed $352, only $10 off from the actual $362, I thought this would be the perfect opportunity for another Wisdom of Crowds test,just like the one conducted 9 months ago and blogged on here.

Two Unilytics employees at the booth, Gary Panchoo and Keith MacDonald, were kind enough to indulge me and my request to compute the average of all the guesses. John Elder was also there, licking his wounds from finished a close second as his guess, $374 was off by $12, a mere $2 away from the winning entry! The results of the analysis are here (summary statistics created by JMP Pro 10 for the mac). In summary, the results are as follows:

Dollar Bill Guess Scores

Method	Guess Value	Error
Actual	362
Ensemble/Average (N=61)	365	3
Winning Guess (person)	352	10
John Elder	374	12
Guess without outlier (2000), 3rd place	338	24
Median, 19th place	275	87

So once again, the average of the entries (the "Crowds" answer) beat the single best entry. What is fascinating to me about this is not that the average won (though this in of itself isn't terribly surprising), but rather how it won. Summary statistics are below. Note that the Median is 275, far below the mean. Not too how skewed the distribution of guesses are (skew = 3.35). The fact that the guesses are skewed positively for a relatively small answer (362) isn't a surprise, but the amount of skew is a bit surprising to me. What these statistics tell us is that while the mean value of the guesses would have been the winner, a more robust statistic would not, meaning that the skew was critical in obtaining a good guess. Or put another way, people more often than not under-guessed by quite a bit (the median is off by 87). Or put a third way, the outlier (2000) which one might naturally want to discount because it was a crazy guess was instrumental to the average being correct. In the prior post on this from July 2011, I trimmed the guesses, removing the "crazy" ones. So when should we remove the wild guesses and when shouldn't we? (If I had removed the 2000, the "average" still would have finished 3rd). I have no answer to when the guesses are not reasonable, but wasn't inclined to remove the 2000 initially here. Full stats from JMP are below, with the histogram showing the amount of skew that exists in this data.

Distribution of Dollar Bill Guesses - Built with JMP

Summary Statistics

Statistic	Guess Value
Mean	365
Std Dev	299.80071
Std Err Mean	38.385548
Upper 95% Mean	441.78253
Lower 95% Mean	288.21747
N	61
Skewness	3.3462476
Median	275
Mode	225
2% Trimmed Mean	331.45614
Interquartile Range	185.5

Note: The mode shown is the smallest of 2 modes with a count of 3.

Quantiles

Quantile		Guess Value
100.0%	maximum	2000
99.5%		2000
97.5%		1546.8
90.0%		751.6
75.0%	quartile	406.5
50.0%	median	275
25.0%	quartile	221
10.0%		145.6
2.5%		98.2
0.5%		96
0.0%	minimum	96

Dilbert, Database marketing and spam

Ruben's comment that referred to spam reminded me of an old Dilbert comic which conveys the misconception about database marketing (e-marketing) and spam.

I know Ruben well and know he was poking fun, though I still have to correct folks who after finding out I do "data mining" actually comment that I'm responsible for spam. Answer: "No, I'm the reason you don't get as much spam!"