This review was first published at ScraperWiki.

Marginalia are an insight into the mind of another reader. This struck me as a I read Data Science for Business by Foster Provost and Tom Fawcett. The copy of the book had previously been read by two of my colleagues. One of whom had clearly read the introductory and concluding chapters but not the bit in between. Also they would probably not be described as a capitalist, “red in tooth and claw”! My marginalia have generally been hidden since I have an almost religious aversion to defacing a book in any way. I do use Evernote to take notes as I go though, so for this review I’ll reveal them here.

Data Science for Business is the book I wasn’t going to read since I’ve already read Machine Learning in Action, Data Mining: Practical Machine Learning Tools and Techniques, and Mining the Social Web. However, I gave in to peer pressure. The pitch for the book is that it is for people who will manage data scientists rather than necessarily be data scientists themselves. The implication here is that you’re paying these data scientists to increase your profits, so you better make sure that’s what they’ll do. You need to be able to understand what data science can and cannot do, ask reasonable questions of data scientists of their models and understand the environment the data scientist needs to thrive.

The book covers several key algorithms: decision trees, support vector machines, logistic regression, k-Nearest Neighbours and term frequency-inverse document frequency (TF-IDF) but not in any great depth of implementation. To my mind it is surprisingly mathematical in places, given the intended audience of managers rather than scientists.

The strengths of the book are in the explanations of the algorithms in visual terms, and in its focus on the expected value framework for evaluating data mining models. Diversity of explanation is always a good thing; read enough different explanations and one will speak directly to you. It also spends more of its time discussing practical applications than other books on data mining. An example on “churn” runs through the book. “Churn” is the loss of customers at the end of a contract, in this case the telecom industry is used as an illustration.

A couple of nuggets I picked up:

• You can think of different machine learning algorithms in terms of the decision boundary they produce and how that looks. Overfitting becomes a decision boundary which is disturbingly intricate. Support vector machines put the decision boundary as far away from the classes they separate as possible;
• You need to make sure that the attributes that you use to build your model will be available at the point of use. That’s to say there is no point in building a model for churn which needs an attribute from a customer which is only available just after they’ve left you. Sounds a bit obvious but I can easily see myself making this mistake;
• The expected value framework for evaluating models. This combines the probability of an event, i.e. the result of a promotion campaign with the value of the outcome. Again churn makes a useful demonstration. If you have the choice between a promotion which is successful with 10 users with an average spend of £10 per year or 1 user with an average spend of £200 then you should obviously go with the latter rather than the former. This reminds me of expectation values in quantum mechanics and in statistical physics.

The title of the book, and the related reading demonstrate that data science, machine learning and data mining are used synonymously. I had a quick look at the popularity of these terms over the last few years. You can see the results in the Google Ngram viewer here. Somewhat to my surprise data science still lags far behind other terms despite the recent buzz, this is perhaps because Google only expose data to 2008.

Which book should you read?

All of them!

If you must buy only one then make it Data Mining, it is encyclopaedic and covers high level business overview, toy implementation and detailed implementation in some depth. If you want to see the code, then get Machine Learning in Action – but be aware that ultimately you are most likely going to be using someone else’s implementation of the core machine learning algorithms. Mining the Social Web is excellent if you want to see the code and are particularly interested in social media. And read Data Science for Business if you are the intended managerial audience or one who will be doing data mining in a commercial environment.

This review was first published at Scraperwiki.

Nate Silver first came to my attention during the 2008 presidential election in the US. He correctly predicted the outcome of the November results in 49 of 50 states, missing only on Indiana where Barack Obama won by just a single percentage point. This is part of a wider career in prediction: aside from a job at KPMG (which he found rather dull), he has been a professional poker player and run a baseball statistics website.

His book The Signal and the Noise: The Art and Science of Prediction looks at prediction in a range of fields: economics, disease, chess, baseball, poker, climate, weather, earthquakes and politics. It is both a review and a manifesto; a manifesto for making better prediction.

The opening chapter is on the catastrophic miscalculation of the default rate for collateralized debt obligations (CDO) which led in large part to the recent financial crash. The theme for this chapter is risk and uncertainty. In this instance, risk means the predicted default rate for these financial instruments and uncertainty means the uncertainty in those predictions. For CDOs the problem was that the estimates of risk were way off, and there was no recognition of the uncertainty in those risk estimates.

This theme of unrecognised uncertainty returns for the prediction of macroeconomic indicators such as GDP growth and unemployment. Here again forecasts are made, and practitioners in the field know these estimates are subject to considerable uncertainty, but the market for prediction, and simple human frailty mean that these uncertainties are ignored. I’ve written elsewhere on both the measurement and prediction of GDP. In brief, both sides of the equation are so fraught with uncertainty that you might as well toss a coin to predict GDP!

The psychology of prediction returns with a discussion of political punditry and the idea of “foxes” and “hedgehogs”. In this context “hedgehogs” are people with one Big Idea who make their predictions based on their Big Idea and are unshiftable from it. In the political arena the “Big Idea” may be ideology but as a scientist I can see that science can be afflicted in the same way. To a man with a hammer, everything is a nail. “Foxes”, on the other hand, are more eclectic and combine a range of data and ideas to predict, as a result they are more successful.

In some senses, the presidential prediction is a catching chickens in a coop exercise. There is quite a bit of data to use and your fellow political pundits are typically misled by their own prejudices, they’re “hedgehogs”, so all you, the “fox” needs to do is calculate in a fairly rational manner and you’re there. Silver returns to the idea of diversity in prediction, combining multiple models, or data of multiple types in his manifesto for better prediction.

There are several chapters on scientific prediction, looking at the predictions of earthquakes, the weather, climate and disease. There is not an overarching theme across these chapters. The point about earthquakes is that precise prediction about where, when and how big appears to be impossible. At short range, the weather is predictable but, beyond a few days, seasonal mean predictions and “the same as today” predictions are as good as the best computer simulations.

Other interesting points raised by this chapter are the ideas of judging predictions by accuracy, honesty and economic value. Honesty in this sense means “is this the best model I can make at this point?”. The interesting thing about weather forecasts in the US is that the National Weather Service makes the results of simulations available to all. Big national value-adders such as the Weather Channel produce a “wet bias” forecast which systematically overstates the possibility of rain for lower values of the chances of rain. This is because customers prefer to be told that there is, say, a 20% chance of rain when it actually turns out to be dry, than be told the actual chance of rain (say 5%) and for it to rain. This “wet bias” gives customers what they want.

Finally, there are predictions on games. On poker, baseball, basketball and chess. The benefits of these systems are the large amounts of data available. The US in particular has a thriving “gaming statistics” culture. The problems are closed, in the sense that there are a set of known rules to each game. And finally, there is ample opportunity for testing predictions against outcomes. This final factor is important in improving prediction.

In technical terms, Silver’s call to arms is for the Bayesian approach to predictions. With this approach, an attempt is made to incorporate prior beliefs into prediction through the use of an estimate of the prior probability and Bayes’ Theorem. Silver exemplifies this with a discussion of prediction in poker. Essentially, poker is about predicting your opponents’ hands and their subsequent actions based on those cards. To be successful, poker players must do this repeatedly and intuitively. However, luck still plays a large part in poker and only the very best players make a profit long term.

The book is structured around a series of interviews with workers in the field, and partly as a result of this it reads rather nicely – it’s not just a dry exposition of facts, but has some human elements too. Though it’s no replacement for a technical reference, it’s worth as a survey of prediction, as well as for the human interest stories.