Archive for the ‘Datamining’ Category
While being able to build predictive models on mountains of data without moving it out of the database is pretty cool in itself, I feel analysis without action is pretty much pointless. Tom Davenport describes this common data mining conundrum in Competing on Analytics.
Many firms are able to segment their customers and determine which ones are most profitable or which are most likely to defect. However, they are reluctant to treat different customers differently—out of tradition or egalitarianism or whatever. With such compunctions, they will have a very difficult time becoming successful analytical competitors—yet it is surprising how often companies initiate analyses without ever acting on them. The “action” stage of any analytical effort is, of course, the only one that ultimately counts.
The OBE tutorial describes a scenario in which a business wants to identify customers who are most likely to purchase insurance. Through a set of simple steps, a (decision tree) classification model is built that can be used to predict whether a particular customer is likely to purchase based on historic data.
In a classical data mining approach, the predictions of this model would be written to some OUTPUT_TABLE where they would be available for subsequent processing. Growing staler every minute—and soon forgotten when its newer sibling OUTPUT_TABLE_NEW_FINAL_2 is inevitably created—our precious business intelligence slowly withers away in a disregarded section of the database until ultimately dropped by a careless DBA.
Output tables are where analytical insight goes to die.
If all we were interested in was building models, we’d be better off glueing choo-choos. It is the new ways in which we can utilise these database resident models that makes this technology really interesting. With a few simple additional steps, this same model can be used in real-time to provide inline predictions based on up-to-date customer data; as well as for new customers.
All we need is a view
and a join.
Update (October 3rd, 2012): as Marcos points out in the comments, I was making things far too complicated. No need for a separate join; simply select the output columns you need and pass everything directly to the view.
The join operations glues the original data and the prediction models together; The view allows us to look at the harmonised results directly. When a customer record is selected from the view the source data for this record is passed to the model to generate the predicted values in real-time. When source data changes so does the prediction. When new source records are added they are automatically processed in the same way.
-- Create a new customer. INSERT INTO INSUR_CUST_LTV_SAMPLE (CUSTOMER_ID, LAST, FIRST) VALUES ('CU123', 'VERMEER', 'LUKAS'); 1 rows inserted. Elapsed: 00:00:00.003 -- Get prediction and probability for the new customer. SELECT CUSTOMER_ID, insur_pred, insur_prob FROM insur_cust_ltv_prediction WHERE CUSTOMER_ID = 'CU123'; CUSTOMER_ID INSUR_PRED INSUR_PROB ----------- ---------- ---------- CU123 No 0.7262813 Elapsed: 00:00:00.004 -- Update customer data. UPDATE INSUR_CUST_LTV_SAMPLE SET bank_funds = 500, checking_amount = 100 WHERE CUSTOMER_ID = 'CU123'; 1 rows updated. Elapsed: 00:00:00.003 -- Get prediction and probability for the updated customer. SELECT CUSTOMER_ID, insur_pred, insur_prob FROM insur_cust_ltv_prediction WHERE CUSTOMER_ID = 'CU123'; CUSTOMER_ID INSUR_PRED INSUR_PROB ----------- ---------- ---------- CU123 Yes 0.6261398 Elapsed: 00:00:00.004
Seamless. Any system that can read data from an Oracle database can now utilise Oracle Data Mining models. No need to move your data. No need to build new applications.
Applications reading data from the view need never know the difference between the original source data and machine generated predictions. Oracle Business Intelligence Publisher can easily display this data in forecasting reports; or use it to power pro-active alerts. In Oracle Real-Time Decisions, rules can be built around the outcomes of these models; or predictions from multiple sources can be fed into combined likelihood models for increased accuracy.
This is huge. Trust me. Stop over-analysing and start taking action. After all, that’s the only step that ultimately counts.
The Wall Street Journal has an interesting article explaining how companies are starting to use (big) data to support their recruiting efforts. It provides a good example of the more general trend in businesses towards evidence-based decisioning and data science, but it also shows how some crucial aspects of these techniques are easily overlooked or oversimplified.
My big-data-science-bogus-alarm started ringing upon reading the last sentence in this short paragraph.
Applicants for the job take a 30-minute test that screens them for personality traits and puts them through scenarios they might encounter on the job. Then the program spits out a score: red for low potential, yellow for medium potential or green for high potential. Xerox accepts some yellows if it thinks it can train them, but mostly hires greens.
Sounds smart, right? Well, maybe.
If Xerox never hires any “reds” and only very few “yellows”, how will they know the program is actually working? How will they know that all that complicated math is doing something more than simply returning random colour values? An evidence-based approach should always include some form of scientific control. If it doesn’t, it might as well be snake oil.
Of course, this is probably just a simple journalistic crime of omission of a trivial implementation detail, but it reminded me of that old chestnut “the tiger repellant”. For your convenience, this blogpost has been equipped with some very strong Tiger Repellant tonic. If you do not see any tigers around you right now, you will know it is working.
See? No tigers?
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Derek Jones posits that “success does not require understanding“.
In my line of work I am constantly trying to understand what is going on (the purpose of this understanding is to control and make things better) and consider anybody who uses machine learning as being clueless, dim witted or just plain lazy; the problem with machine learning is that it gives answers without explanations (ok decision trees do provide some insights).
Problem solving versus solving problems.
As one who specializes in using machine learning, I obviously resent being called “clueless, dim witted or just plain lazy”. However, I feel a larger point should be made here. Success does most definitely require understanding, but not necessarily of how one particular instance of a solution came about.
To be successful in any machine learning effort, one needs to have intricate understanding of what the problem is and how techniques can be applied to find solutions. This is a more general form of understanding which puts more emphasis on the process of finding workable models, rather than on applying these models to individual instances of a problem. Comprehension of problem solving over understanding a particular solution.
Driving a black box.
Consider the following example. To me, the engine of my car is a black box; I have very little idea how it works. My mechanic does know how engines work in general, but he is unable to know the exact internal state of the engine in my car as I am cruising down the highway at 100 miles per hour. None of this “lack of understanding” prevents me from getting from A to B. I turn the wheel, I push the peddel and off we go.
In essence, my mechanic and I have different levels of understanding of my car. But importantly, at different levels of precision, the thing becomes a black box to each of us; in the sense that there is a point where our otherwise perfectly practical models break down and no longer are able to reflect reality. In the end, it’s black boxes all the way down.
Models are merely tools to help you navigate a vastly complex world. Very much like machine learning models, a scientific model might work in many cases, but so does Newton’s law of universal gravitation. We know for a fact that that particular model is definitely wrong; and I sincerely hope many others are just as incorrect.
There will always be limits to our understanding. The fact that we have a model that can help us predict does not necessarily mean we have correctly understood the nature of the universe. All models are wrong, but some are useful.
Reality is simply much too complicated to be captured in a manageable set of rules, but even incomplete (or incorrect) models can provide insight and help us better navigate this world. Machine learning is successful, precisely because it can help us find such models.
[ Peter Norvig has written an excellent piece on this subject in relation to language models. ]
Without fail, a company will employ a recommendation engine for a purpose (nobody does this for fun, really). Often, that purpose is profit (or something along those line). For most companies, ‘relevance’ is irrelevant (no pun intended).
The success of any recommendation engine should (in my opinion) be measured by its ability to meet the objectives it was intended to achieve. As said, in most cases, this will be tied to sales or profit.
A/B test your system against a control (often random, might be rules). If your recommendations increase sales (or decrease costs, or decrease call handeling time, or increase revenue, or increase customer satisfaction, etc) compared to the alternative you’re doing pretty good. You can forget about the rest.
Who cares about relevance if you can measure business value?
[ Posted on Quora as answer to What is the best way to test the relevance of a recommendation engine? ]
James Taylor is spot-on.
Too many analytic professionals think that only the data speaks and that business rules are, as someone once said to me, “for people too stupid to analyze their data”. Similarly too many IT professionals think that everything can be reduced to business rules or to code using explicit analysis. The reality for most decisions is somewhere in between.
In order to truly achieve business transcendence one must follow the Middle Way.
There are two types of people in this world:
- Those who can extrapolate from incomplete data.
[ Via @professorkitteh. Original source unknown. ]
A few months ago my friend and neighbor Olav was fiddling around with a dataset of movie plot descriptions he downloaded from the Internet Movie Database (IMDb). If I recall correctly, he was taking a stab at the Netflix Prize. We discussed this for a while over coffee, but (as usual) our conversations were all over the place; and somewhere along the line we wondered what songs are used most often in movies.
What is that song they always play? The one that goes like ‘#dun dun dun dun dudun dun dun duuuuun#‘. You know?
The IMDb site offers lots of different datasets for download, and we quickly found that one of them contains soundtrack listings (the aptly named file soundtracks.list.gz). Now it was just a matter of filtering out the unnecessary contextual data and counting songs. Quickly Olav, who does datamining for a living, managed to get all this done using spiffy point-and-click tools. I proceeded to ask twitter what people thought the answer would be.
The top five results turned out to be a collection of classics. The songs played in movies (according to the IMDb data) is as follows.
- “Jingle Bells” (220x)
- “William Tell Overture” (204x)
- “Home Sweet Home” (160x)
- “Auld Lang Syne” (149x)
- “Rock-a-Bye Baby” (140x)
Not at all what we were expecting, but quite obvious when you think about how many Christmas movies are out there. Data mining is very often like that. You find answers that were unexpected, but also unsurprisingly obvious.
It’s the same song, but it never gets old.
[Much later, a friend (can’t remember exactly who) noted that the song that is played most often in theaters is probably not listed in the data set the IMDb provides. It’s the 20th Century Fox intro.]