## Data science and sensemaking – tales from two hackathons

“*It isn’t that they can’t see the solution. It is that they can’t see the problem*” – GK Chesterton

### Introduction

Examples of vendor-generated hype about data science are not hard to find, I found one on the very first site I visited: a large technology and services vendor who, in their own words, claim their analytics solutions help organisations “*engage with data to answer the toughest business questions, uncover patterns and pursue breakthrough ideas.*” I’ve deliberately avoided linking to the guilty party because there are many others that spout similar rhetoric.

Unfortunately it seems to work: according to Gartner, “*by 2020, predictive and prescriptive analytics will attract 40% of enterprises’ net new investment in business intelligence and analytics.*” This trend is accompanied by a concomitant increase in demand for data science education, fuelled by remarks along the lines that data science is “*The Sexiest Job of the 21 ^{st} Century*.”

By and large, data science education tends to focus on algorithms and technology, but its practice involves much more. The vendor who claims that technology can help organisations grapple with “*toughest business questions*” and “*pursue breakthrough ideas*” is singularly silent about where these questions or ideas come from. Data is meaningless without a meaningful hypothesis. Problem is, in the real world questions or hypotheses aren’t obvious; one has to work to formulate them. As the management icon Russell Ackoff once said, “*Outside of school, problems are seldom given; they have to be taken, extracted from complex situations…”*

The art of taking problems is what sensemaking is all about.

Unfortunately, it is a skill that is typically ignored by data science educators.

Why?

Probably because it is hard to teach…but the good news is that it can be learnt. Like most tacit skills, sensemaking is best learnt by doing, that is, by formulating problems in real-world situations. Before I get to that, however, let’s take a brief detour.

### Real world problems are characterised by ambiguity

An important aspect of real-world problems – as opposed to classroom ones – is that they are invariably fraught with ambiguity. For example, a customer’s requirements may be vague or the available data incomplete and messy. What this means is that there is no guarantee one will be able to formulate a well-posed problem, let alone get a useful answer. Worse, unlike a risk-based situation in which uncertainty can be quantified, one cannot even figure out the odds of success.

The human brain processes quantifiable uncertainty (aka risk) and ambiguity very differently. The former, which can be calculated, is dealt with by the prefrontal cortex which is responsible for decision making and goal-oriented thinking. Ambiguity, on the other hand, is processed by the amygdala, which deals with emotions. The upshot of this is that *ambiguity evokes an emotional response*, the most common one being *anxiety*.

Although some people are innately better at coping with anxiety than others, it is possible to get better at it by repeatedly putting oneself in high-pressure (yet safe) situations that are ambiguous. For data science students, hackathons provide a perfect opportunity to do this.

### Ambiguity in data science – tales from two hackathons

Over the last two months, I’ve had the privilege of being a part of the Master of Data Science Innovation (MDSI) program run by the Connected Intelligence Centre at UTS. The course director, Theresa Anderson, sees hackathons as a great way for students to learn how to handle ambiguity. So, apart from regular coursework assignments, students are encouraged to participate in external hackathons sponsored by industry and government organisations. This gives them opportunities to gain practical experience in formulating problems in ambiguous and high-pressure environments.

#### Datacake at GovHack

A few MDSI student teams participated in a GovHack event earlier this year. Here’s what William Azevedo, a member of team that called themselves Datacake, wrote about his team’s problem formulation journey at the event :

The challenge is simple: the competitors should form teams, identify a problem and use data from government agencies from Australia and New Zealand to present a solution to the problem. Naturally, this solution should bring some benefit to the society.

I’m not sure I’d use the word *simple*…but the importance of problem formulation comes through quite clearly. Here’s how he and his team (called Datacake) went about it:

As a starting point, our team published an online survey to understand how safe people feel when walking on the streets, especially at night. As we didn’t have much time, we spread the message via social networks. In a couple of hours, we received 44 answers. It gave us enough information to back our idea.

Notice the process used in defining the problem – the team realised they did not know enough to define a meaningful problem so they went and got relevant data. Following this:

Our team analysed the answers of the survey, engaged in passionate discussions, took tips from the mentors, had lots of coffee and designed some cool diagrams on the blackboard.

…and then his description of the Aha moment when a good idea emerged:

Then the magic happened. We had this idea of merging information about crime, demographics, weather, land zoning and street illumination to provide a map of the safe and unsafe areas within a suburb.

An important point is that sensemaking is best done collaboratively. Since the problem is ambiguous or even undefined (as in this case) no individual has a privileged access to the “truth.” It is therefore important to bring diverse perspectives to bear on the problem. Indeed, sensemaking may be thought of as *collaborative problem formulation and solving*. In view of this it is interesting to hear what other members of Team Datacake had to say about their problem formulation process. Here’s a comment from Anthony So:

During the whole weekend we really forced ourselves to go deep and asked “Why is it happening? Why is it happening? Why is it happening?” every time we found an interesting pattern. We really wanted to understand the true root causes of those accidents. We didn’t want to stay at a descriptive level. We knew the answers were behavioural. We knew there were multiple problems and therefore require different answers and solutions. We did different techniques to do so: machine learning, stats, data visualisation. It didn’t matter which we used the only important point was how can we get to answers of those questions.

The specific area they looked at was pedestrian safety. They found that obvious variables, such as driver fatigue and hazards were not significant, so they started looking for other potential factors. Here’s how Anthony put it:

For instance we built a classification model on the severity of the accidents involving children but we didn’t use it to make predictions. We used it to identify the important features (and unimportant) for those cases. We found out that some of the variables related to the environment (Primary_hazardous_feature, Surface_condition, Weather…) and to the drivers (Fatigue_involved_in_crash…) were not important. This gave us a good indication that those accidents are mostly related directly to the behaviour of the children. So we kept diving further and further and found 3 postcodes with higher numbers of accidents than others. We focused on those 3 areas and we kept going deeper and deeper…

In the end Datacake came up with a few suggestions for improving pedestrian safety. They were awarded a prize for their efforts, so the problem they formulated and solved was clearly valuable to the sponsors.

#### Peppermoney Hackathon

A couple of weekends ago, Pepper Money, Australia’s largest non-bank lender sponsored a day long internal hackathon for MDSI students, with a hefty winner-take-all prize as an incentive. The challenge was quite open-ended, and had to do with helping the organisation develop a consistent brand voice. Participants were given a small corpus of text files from the organisation’s public and social media sites and were given very general guidelines on how to proceed. Details were left entirely to the teams.

As one might expect, most teams spent the first few hours struggling to define a relevant and tractable problem – relevance being paramount for the client and tractability for the teams. Being a mentor at the event, I was able observe how different teams handled this. Among other things, I was particularly impressed by how some teams with very little text mining experience were able to – in a few hours – come up with a good problem, an approach to solve it…and, most importantly, make decent progress by day’s end.

I won’t go into details except to say that the approaches were diverse, ranging from the somewhat philosophical to the very technical. A couple of examples:

- Using Aristotle’s notion of modes of persuasion to analyse and evaluate marketing material.
- Drawing on deep learning technology (Recurrent Neural Networks via Theano) to build a brand voice generator.

I was amazed at the diversity of solutions the groups came up with, and so were the other mentors and the sponsor. Blair Hudson, Innovation Portfolio Manager at Pepper Money, summed the day up very well when he said:

#PepxUTS was our first hackathon event, challenging students to build data science solutions in a day to allow everyone at Pepper to communicate using a consistent brand voice. Our Co-Group CEOs both joined in for judging and awarded the winners. It was a rewarding day for all involved

(For some vignettes from the day, check out the #PepxUTS hashtag on Twitter.)

The day’s experiences left me ever more convinced that hackathons are an excellent vehicle for learning and demonstrating the practical utility of sensemaking skills.

### Wrapping up

The two case studies highlight the benefits of sensemaking skills, both for students and organisations. On the one hand, students who participated got valuable experience in formulating problems collaboratively in high-pressure, high-ambiguity situations. This is a skill that cannot be learnt in classrooms, MOOCs or even in online data challenges (like Kaggle) where problems tend to be clearly defined. On the other hand, sponsoring organisations have benefited from new insights into longstanding problems.

Finally, it should be clear that although I’ve focused on educational settings, what I’ve said for students applies to organisational settings too: there’s nothing to stop organisations from using hackathons as a means to help their employees learn sensemaking skills.

To conclude, the main point I want to make is that the most important situations we encounter at work (and even in our personal lives) are usually fraught with ambiguity. Our first reaction is to jump into problem solving mode because it feels like the right thing to do. In reality, one is generally better off stepping back and taking the time to think the situation through, preferably with a group of diversely skilled individuals. All too often this sensemaking step is neglected, and teams end up solving an irrelevant problem.

To paraphrase Chesterton, in order to see the right solution, one must first see the right problem.

**Acknowledgements**

Many thanks to Blair Hudson, William Azevedo and Anthony So for their contributions to this piece.

## A gentle introduction to random forests using R

### Introduction

In a previous post, I described how decision tree algorithms work and demonstrated their use via the rpart library in R. Decision trees work by splitting a dataset *recursively*. That is, subsets arising from a split are further split until a predetermined termination criterion is reached. At each step, a split is made based on the independent variable that results in the *largest possible reduction in heterogeneity* *of the dependent variable*.

(*Note*: readers unfamiliar with decision trees may want to read that post before proceeding)

The main drawback of decision trees is that they are prone to overfitting. The reason for this is that trees, if grown deep, are able to fit all kinds of variations in the data, including noise. Although it is possible to address this partially by pruning, the result often remains less than satisfactory. This is because the algorithm makes a locally optimal choice at each split without any regard to whether the choice made is the best one overall. A poor split made in the initial stages can thus doom the model, a problem that cannot be fixed by post-hoc pruning.

In this post I describe random forests, a tree-based algorithm that addresses the above shortcoming of decision trees. I’ll first describe the intuition behind the algorithm via an analogy and then do a demo using the R randomForest library.

### Motivating random forests

One of the reasons for the popularity of decision trees is that they reflect the way humans make decisions: by weighing up options at each stage and choosing the best one available. The analogy is particularly useful because it also suggests how decision trees can be improved.

One of the lifelines in the game show, Who Wants to be A Millionaire, is “Ask The Audience” wherein a contestant can ask the audience to vote on the answer to a question. The rationale here is that the majority response from a large number of independent decision makers is more likely to yield a correct answer than one from a randomly chosen person. There are two factors at play here:

- People have different experiences and will therefore draw upon different “data” to answer the question.
- People have different knowledge bases and preferences and will therefore draw upon different “variables” to make their choices at each stage in their decision process.

Taking a cue from the above, it seems reasonable to build many decision trees using:

- Different sets of training data.
- Randomly selected subsets of variables at each split of every decision tree.

Predictions can then made by taking the majority vote over all trees (for classification problems) or averaging results over all trees (for regression problems). This is essentially how the random forest algorithm works.

The net effect of the two strategies is to reduce overfitting by a) averaging over trees created from different samples of the dataset and b) decreasing the likelihood of a small set of strong predictors dominating the splits. The price paid is reduced interpretability as well as increased computational complexity. But then, there is no such thing as a free lunch.

### The mechanics of the algorithm

Although we will not delve into the mathematical details of the algorithm, it is important to understand how two points made above are implemented in the algorithm.

#### Bootstrap aggregating… and a (rather cool) error estimate

A key feature of the algorithm is the use of multiple datasets for training individual decision trees. This is done via a neat statistical trick called **b**ootstrap **agg**regat**ing** (also called *bagging*).

Here’s how bagging works:

Assume you have a dataset of size *N*. From this you create a sample (i.e. a subset) of size *n* (*n less than or equal to N*) by choosing n data points randomly with replacement. “Randomly” means every point in the dataset is equally likely to be chosen and “with replacement” means that a specific data point can appear more than once in the subset. Do this *M* times to create *M* equally-sized samples of size *n* each. It can be shown that this procedure, which statisticians call bootstrapping, is legit when samples are created from large datasets – that is, when *N* is large.

Because a bagged sample is created by selection with replacement, there will generally be some points that are not selected. In fact, it can be shown that, on the average, each sample will use about two-thirds of the available data points. This gives us a clever way to estimate the error as part of the process of model building.

Here’s how:

For every data point, obtain predictions for trees in which the point was *out of bag*. From the result mentioned above, this will yield approximately *M/3* predictions per data point (because a third of the data points are out of bag). Take the majority vote of these *M/3 *predictions as the predicted value for the data point. One can do this for the entire dataset. From these out of bag predictions for the whole dataset, we can estimate the overall error by computing a classification error (Count of correct predictions divided by N) for classification problems or the root mean squared error for regression problems. This means there is no need to have a separate test data set, which is kind of cool. However, if you have enough data, it is worth holding out some data for use as an independent test set. This is what we’ll do in the demo later.

#### Using subsets of predictor variables

Although bagging reduces overfitting somewhat, it does not address the issue completely. The reason is that in most datasets a small number of predictors tend to dominate the others. These predictors tend to be selected in early splits and thus influence the shapes and sizes of a significant fraction of trees in the forest. That is, strong predictors enhance correlations between trees which tends to come in the way of variance reduction.

A simple way to get around this problem is to use a *random subset of variables at each split*. This avoids over-representation of dominant variables and thus creates a more diverse forest. This is precisely what the random forest algorithm does.

### Random forests in R

In what follows, I use the famous Glass dataset from the mlbench library. The dataset has 214 data points of six types of glass with varying metal oxide content and refractive indexes. I’ll first build a decision tree model based on the data using the rpart library (recursive partitioning) that I covered in an earlier article and then use then show how one can build a random forest model using the randomForest library. The rationale behind this is to compare the two models – single decision tree vs random forest. In the interests of space, I won’t explain details of the rpart here as I’ve covered it at length in the previous article. However, for completeness, I’ll list the demo code for it before getting into random forests.

#### Decision trees using rpart

Here’s the code listing for building a decision tree using rpart on the Glass dataset (please see my previous article for a full explanation of each step). Note that I have not used pruning as there is little benefit to be gained from it (*Exercise for the reader*: try this for yourself!).

Now, we know that decision tree algorithms tend to display high variance so the hit rate from any one tree is likely to be misleading. To address this we’ll generate a bunch of trees using different training sets (via random sampling) and calculate an average hit rate and spread (or standard deviation).

The decision tree algorithm gets it right about 69% of the time with a variation of about 5%. The variation isn’t too bad here, but the accuracy has hardly improved at all (*Exercise for the reader*: why?). Let’s see if we can do better using random forests.

#### Random forests

As discussed earlier, a random forest algorithm works by averaging over multiple trees using bootstrapped samples. Also, it reduces the correlation between trees by splitting on a random subset of predictors at each node in tree construction. The key parameters for randomForest algorithm are the number of trees (*ntree*) and the number of variables to be considered for splitting (*mtry*). The algorithm sets a *default of 500 for ntree* and sets *mtry* to *one-third the total number of predictors for classification* problems and *square root of the the number of predictors for regression. *These defaults can be overridden by explicitly providing values for these variables.

The preliminary stuff – the creation of training and test datasets etc. – is much the same as for decision trees but I’ll list the code for completeness.

randomForest(formula = Type ~ ., data = trainGlass, importance = TRUE, xtest = testGlass[, -typeColNum], ntree = 1001)

1 | 2 | 3 | 5 | 6 | 7 | class.error | |

1 | 40 | 7 | 2 | 0 | 0 | 0 | 0.1836735 |

2 | 8 | 49 | 1 | 2 | 2 | 1 | 0.2222222 |

3 | 6 | 3 | 6 | 0 | 0 | 0 | 0.6000000 |

5 | 0 | 1 | 0 | 11 | 0 | 1 | 0.1538462 |

6 | 1 | 2 | 0 | 1 | 6 | 0 | 0.5000000 |

7 | 1 | 2 | 0 | 1 | 0 | 21 | 0.1600000 |

The first thing to note is the out of bag error estimate is ~ 24%. Equivalently the hit rate is 76%, which is better than the 69% for decision trees. Secondly, you’ll note that the algorithm does a terrible job identifying type 3 and 6 glasses correctly. This could possibly be improved by a technique called boosting, which works by iteratively improving poor predictions made in earlier stages. I plan to look at boosting in a future post, but if you’re curious, check out the gbm package in R.

Finally, for completeness, let’s see how the test set does:

1 | 2 | 3 | 5 | 6 | 7 | |

1 | 19 | 2 | 0 | 0 | 0 | 0 |

2 | 1 | 9 | 1 | 0 | 0 | 0 |

3 | 1 | 1 | 1 | 0 | 0 | 0 |

5 | 0 | 1 | 0 | 0 | 0 | 0 |

6 | 0 | 0 | 0 | 0 | 3 | 0 |

7 | 0 | 0 | 0 | 0 | 0 | 4 |

The test accuracy is better than the out of bag accuracy and there are some differences in the class errors as well. However, overall the two compare quite well and are significantly better than the results of the decision tree algorithm.

### Variable importance

Random forest algorithms also give measures of *variable importance*. Computation of these is enabled by setting *importance, *a boolean parameter, to TRUE. The algorithm computes two measures of variable importance: *mean decrease in Gini* and *mean decrease in accuracy*. Brief explanations of these follow.

#### Mean decrease in Gini

When determining splits in individual trees, the algorithm looks for the largest class (in terms of population) and attempts to isolate it first. If this is not possible, it tries to do the best it can, always focusing on isolating the largest remaining class in every split.This is called the Gini splitting rule (see this article for a good explanation of the rule).

The “goodness of split” is measured by the Gini Impurity, . For a set containing K categories this is given by:

where is the fraction of the set that belongs to the ith category. Clearly, is 0 when the set is homogeneous or pure (1 class only) and is maximum when classes are equiprobable (for example, in a two class set the maximum occurs when and are 0.5). At each stage the algorithm chooses to split on the predictor that leads to the largest decrease in . The algorithm tracks this decrease for each predictor for all splits and all trees in the forest. The average is reported as the *mean decrease in Gini.*

#### Mean decrease in accuracy

The mean decrease in accuracy is calculated using the out of bag data points for each tree. The procedure goes as follows: when a particular tree is grown, the out of bag points are passed down the tree and the prediction accuracy (based on all out of bag points) recorded . The predictors are then randomly permuted and the out of bag prediction accuracy recalculated. The decrease in accuracy for a given predictor is the difference between the accuracy of the original (unpermuted) tree and the those obtained from the permuted trees in which the predictor was excluded. As in the previous case, the decrease in accuracy for each predictor can be computed and tracked as the algorithm progresses. These can then be averaged by predictor to yield a mean decrease in accuracy.

#### Variable importance plot

From the above, it would seem that the mean decrease in accuracy is a more global measure as it uses fully constructed trees in contrast to the Gini measure which is based on individual splits. In practice, however, there could be other reasons for choosing one over the other…but that is neither here nor there, if you set importance to TRUE, you’ll get both. The numerical measures of importance are returned in the randomForest object (Glass.rf in our case), but I won’t list them here. Instead, I’ll just print out the variable importance plots for the two measures as these give a good visual overview of the relative importance of variables. The code is a simple one-liner:

The plot is shown in Figure 1 below.

In this case the two measures are pretty consistent so it doesn’t really matter which one you choose.

### Wrapping up

Random forests are an example of a general class of techniques called ensemble methods. These techniques are based on the principle that averaging over a large number of not-so-good models yields a more reliable prediction than a single model. This is true only if models in the group are independent of each other, which is precisely what bootstrap aggregation and predictor subsetting are intended to achieve.

Although considerably more complex than decision trees, the logic behind random forests is not hard to understand. Indeed, the intuitiveness of the algorithm together with its ease of use and accuracy have made it very popular in the machine learning community.

## The Heretic’s Guide to Management – understanding ambiguity in the corporate world

I am delighted to announce that my new business book, *The Heretic’s Guide to Management: The Art of Harnessing Ambiguity*, is now available in e-book and print formats. The book, co-written with Paul Culmsee, is a loose sequel to our previous tome, *The Heretics Guide to Best Practices*.

Many reviewers liked the writing style of our first book, which combined rigour with humour. This book continues in the same vein, so if you enjoyed the first one we hope you might like this one too. The new book is half the size of the first one and I considerably less idealistic too. In terms of subject matter, I could say “*Ambiguity, Teddy Bears and Fetishes*” and leave it at that…but that might leave you thinking that it’s not the kind of book you would want anyone to see on your desk!

Rest assured, *The Heretic’s Guide to Management* is not a corporate version of Fifty Shades of Grey. Instead, it aims to delve into the complex but fascinating ways in which *ambiguity* affects human behaviour. More importantly, it discusses how ambiguity can be harnessed in ways that achieve positive outcomes. Most management techniques (ranging from strategic planning to operational budgeting) attempt to *reduce* ambiguity and thereby provide clarity. It is a profound irony of modern corporate life that they often end up doing the opposite: increasing ambiguity rather than reducing it.

On the surface, it is easy enough to understand why: organizations are complex entities so it is unreasonable to expect management models, such as those that fit neatly into a 2*2 matrix or a predetermined checklist, to work in the real world. In fact, expecting them to work as advertised is like colouring a paint-by-numbers Mona Lisa, expecting to recreate Da Vinci’s masterpiece. Ambiguity therefore invariably remains untamed, and reality reimposes itself no matter how alluring the model is.

It turns out that most of us have a deep aversion to situations that involve even a hint of ambiguity. Recent research in neuroscience has revealed the reason for this: ambiguity is processed in the parts of the brain which regulate our emotional responses. As a result, many people associate it with feelings of *anxiety*. When kids feel anxious, they turn to transitional objects such as teddy bears or security blankets. These objects provide them with a sense of stability when situations or events seem overwhelming. In this book, we show that as grown-ups we don’t stop using teddy bears – it is just that the teddies we use take a different, more corporate, form. Drawing on research, we discuss how management models, fads and frameworks are actually akin to teddy bears. They provide the same sense of comfort and certainty to corporate managers and minions as real teddies do to distressed kids.

Most children usually outgrow their need for teddies as they mature and learn to cope with their childhood fears. However, if development is disrupted or arrested in some way, the transitional object can become a *fetish* – an object that is held on to with a pathological intensity, simply for the comfort that it offers in the face of ambiguity. The corporate reliance on simplistic solutions for the complex challenges faced is akin to little Johnny believing that everything will be OK provided he clings on to Teddy.

When this happens, the trick is finding ways to help Johnny overcome his fear of ambiguity.

Ambiguity is a primal force that drives much of our behaviour. It is typically viewed negatively, something to be avoided or to be controlled.

The truth, however, is that ambiguity is a force that can be used in positive ways too. The Force that gave the Dark Side their power in the Star Wars movies was harnessed by the Jedi in positive ways.

Our book shows you how ambiguity, so common in the corporate world, can be harnessed to achieve the results you want.

The e-book is available via popular online outlets. Here are links to some:

For those who prefer paperbacks, the print version is available here.

Thanks for your support 🙂

## The story before the story – a data science fable

It is well-known that data-driven stories are a great way to convey results of data science initiatives. What is perhaps not as well-known is that data science projects often have to *begin* with stories too. Without this “story before the story” there will be no project, no results and no data-driven stories to tell….

For those who prefer to read, here’s a transcript of the video in full:

In the beginning there is no data, let alone results…but there are ideas. So, long before we tell stories about data or results, we have to tell stories about our ideas. The aim of these stories is to get people to care about our ideas as much as we do and, more important, invest in them. Without their interest or investment there will be no results and no further stories to tell.

So one of the first things one has to do is craft a story about the idea…or the story before the story.

Once upon a time there was a CRM system. The system captured every customer interaction that occurred, whether it was by phone, email or face to face conversation. Many quantitative details of interactions were recorded, time, duration, type. And if the interaction led to a sale, the details of the sale were recorded too.

Almost as an aside, the system also gave sales people the opportunity to record their qualitative impressions as free text notes. As you might imagine, this information, though potentially valuable, was never analysed. Sure managers looked at notes in isolation from time to time when referring.to specific customer interactions, but there was no systematic analysis of the corpus as a whole. Nobody had thought it worthwhile to do this, possibly because it is difficult if not quite impossible to analyse unstructured information in the world of relational databases and SQL.

One day, an analyst was browsing data randomly in the system, as good analysts sometimes do. He came across a note that to him seemed like the epitome of a good note…it described what the interaction was about, the customer’s reactions and potential next steps all in a logical fashion.

This gave him an idea. Wouldn’t it be cool, he thought, if we could measure the quality of notes? Not only would this tell us something about the customer and the interaction, it may tell us something about the sales person as well.

The analyst was mega excited…but he realised he’d need help. He was an IT guy and as we all know, business folks in big corporations stopped listening to their IT guys long ago. So our IT guy had his work cut out for him.

After much cogitation, he decided to enlist the help of his friend, a strategic business analyst in the marketing department. This lady, who worked in marketing had the trust of the head of marketing. If she liked the idea, she might be able to help sell it to the head of marketing.

As it turned out, the business analyst loved the idea…more important, since she knew what the sales people do on a day to day basis, she could give the IT guy more ideas on how he could build quantitative measures of the quality of notes. For example, she suggested looking for emotion-laden words or mentions of competitor’s products and so on. The IT guy now had some concrete things to work on. The initial results gave them even more ideas, and soon they had more than enough to make a convincing pitch to the head of marketing.

It would take us too far afield to discuss details of the pitch, but what we will say is this: they avoided technical details, instead focusing on the strategic and innovative aspects of the work.

The marketing head liked the idea…what was there not to like? He agreed to support the effort, and the idea became a project….

…and yes, within months the project resulted in new insights into customer behaviour. But that is another story.

## The hidden costs of IT outsourcing

Many outsourcing arrangements fail because customers do not factor in hidden costs. In 2009, I wrote a post on these hard-to-quantify transaction costs. The following short video (4 mins 45 secs) summarises the main points of that post in a (hopefully!) easy-to-understand way:

—

*Note*: Here’s the full script, for those who prefer to read instead of watching…

One of the questions that organisations grapple with is whether or not to outsource IT work to external vendors. The work of Oliver Williamson a Nobel Laureate in Economics – provides some insight into this issue. This video is a brief look at how Williamson’s work on transaction cost economics can be applied to the question of outsourcing IT development or implementation.

A firm has two choices for any economic activity: it can either perform the activity in-house or go to market. In either case, the cost of the activity can be decomposed into *production costs*, which are direct and indirect costs of producing the good or service, and transaction costs, which are costs associated with making the economic exchange (more on this in a minute).

In the case of in-house IT work production costs include salaries, equipment costs etc whereas transaction costs include costs relating to building an IT team (with the right skills, attitude and knowledge).

In the case of outsourced IT work, production costs are similar to those in the in-house case – except that they are now incurred by the vendor and passed on to the client. The point is, these costs are generally known upfront.

The transaction costs, however, are significantly different. They include things such as:

**Search costs**: cost of searching for a suitable vendor**Bargaining costs**: effort incurred in agreeing on an acceptable price.**Enforcement costs**: costs of ensuring compliance with the contract**Costs of coordinating work****Cost of uncertainty**: cost associated with unforeseen changes (scope change is a common example)

Now, there are a couple of things to note about transaction costs for outsourcing arrangements:

Firstly, they are typically the client’s problem, not the vendors. Secondly, they can be very hard to figure out upfront. They are the therefore the *hidden costs of outsourcing*.

According to Williamson, the decision as to whether or not an economic activity should be outsourced depends critically on these hidden transaction costs. In his words, “*The most efficient institutional arrangement for carrying out a particular economic activity would be the one that minimized transaction costs*.”

The most efficient institutional arrangement for IT development work is often the market, but in-house arrangements are sometimes better.

The potentially million dollar question is: when are in-house arrangements better?

Williamson’s work provides an answer to this question. He argues that the cost of completing an economic transaction in an open market depends on two factors

**Complexity of the transaction**– for example, implementing an ERP system is more complex than implementing a new email system.**Asset specificity**– this refers to the degree of customization of the service or product. Highly customized services or products are worth more to the two parties than to anyone else. For example, custom IT services, tailored to the requirements of a specific company have more value client and provider than to anyone else.

In essence, the transaction costs increase with complexity and degree of customization. From this we can conclude that in-house arrangements may be better for work that is complex or highly customized. The reason for this is simple: it is difficult to specify such systems in detail upfront. Consequently, contracts for such work tend to be complex…and worse, they invariably leave out important details.

Such contracts will work only if interpreted in a *farsighted manner*, with disputes being settled directly between the vendor and client instead of resorting to litigation. When this becomes too hard to do, it makes sense to carry out the activity in-house. Note that this does not mean that it has to be done by internal staff…one can still hire contractors, but it is important ensure that they remain under internal supervision.

If one chooses to outsource such work it is important to ensure that the contract is as unambiguous and transparent as possible. Moreover, both the client and the vendor should *expect* omissions in contracts, and be flexible whenever there are disagreements over the interpretation of contract terms. In this end, this is possible only if there is a trust-based relationship between the client and vendor…and trust, of course, is impossible to contractualise.

To sum up: be wary of outsourcing work that is complex or highly customized…and if you must, be sure to go with a vendor you trust.

## What is sensemaking?

I’ve recently set up a consulting practice specializing in sensemaking and analytics. Most people understand the *analytics* bit, but many have questions about *sensemaking*. I got that question so many times that I decided to do a short (2.5 minute) whiteboard video explaining what the term means to me (and my definition is not the same as Wikipedia’s).

Here it is:

For those who prefer the written word, here’s the script (minus the advertising):

—

“Most organizations are very good at solving problems. This is no surprise: much of training, right from school to university, focuses on teaching us the skills required to solve problems. Now regardless of the specific technique used, the problem-solving process is essentially a logical or analytical one. It goes something like this:

**G**ather information about the problem.**A**nalyse the information.**F**ormulate candidate solutions.**I**mplement the solution of choice.

This so-called GAFI method works by breaking problems down into manageable parts, solving each of the parts separately and then assembling these into a solution. The method works very well for most scientific and engineering problems – even one as complicated as sending a spacecraft to Saturn. Indeed, it is so successful that it underpins all of science and modern technology.

However, there is a serious gap in the GAFI method – it assumes that problems are given, it does not tell us how to formulate problems. And as the management luminary, Russell Ackoff once said:

“*Outside of school, problems are seldom given; they have to be taken, extracted from complex situations…”*

The art of taking problems is what sensemaking is all about.

Unlike analytical thinking, which is purely logical, sensemaking involves such as *collaboration*, *imagination* and a healthy *tolerance for ambiguity*. It is an art that is absolutely essential for surviving…no, *thriving*, in the increasingly complex world of the 21^{st} century.

The two modes of thinking – sensemaking and analytical – are as different as chalk and cheese but both are necessary for a successful outcome. We like to think of them as lying at opposite ends of a spectrum of thinking styles. When approaching a new situation or problem, one should always begin at the sensemaking end and move towards the analytical end as one understands the problem better. Unfortunately time pressures in corporate environments often force managers and employees into analytical mode without a full appreciation of the problem they are attempting solve. As a result the solutions are often less than optimal. Sensemaking techniques equip organisations with tools that cover the entire problem lifecycle, from definition to solution.”

—

As a closing remark (that might be construed as advertising…) I’ll mention that I’ve discussed a number of these techniques on Eight to Late. Here are a couple of examples:

The Approach: a dialogue mapping story

…and, of course, you can always have a look at my book or ping me for a no-obligation chat to find out more 🙂

## A gentle introduction to decision trees using R

### Introduction

Most techniques of predictive analytics have their origins in probability or statistical theory (see my post on Naïve Bayes, for example). In this post I’ll look at one that has more a commonplace origin: the way in which humans make decisions. When making decisions, we typically identify the options available and then evaluate them based on criteria that are important to us. The intuitive appeal of such a procedure is in no small measure due to the fact that it can be easily explained through a visual. Consider the following graphic, for example:

(Original image: https://www.flickr.com/photos/dullhunk/7214525854, Credit: Duncan Hull)

The tree structure depicted here provides a neat, easy-to-follow description of the issue under consideration and its resolution. The decision procedure is based on asking a series of questions, each of which serve to further reduce the domain of possibilities. The predictive technique I discuss in this post,classification and regression trees (CART), works in much the same fashion. It was invented by Leo Breiman and his colleagues in the 1970s.

In what follows, I will use the open source software, R. If you are new to R, you may want to follow this link for more on the basics of setting up and installing it. Note that the R implementation of the CART algorithm is called RPART (Recursive Partitioning And Regression Trees). This is essentially because Breiman and Co. trademarked the term CART. As some others have pointed out, it is somewhat ironical that the algorithm is now commonly referred to as RPART rather than by the term coined by its inventors.

### A bit about the algorithm

The *rpart* algorithm works by splitting the dataset recursively, which means that the subsets that arise from a split are further split until a predetermined termination criterion is reached. At each step, the split is made based on the independent variable that results in the *largest possible reduction in heterogeneity* *of the dependent (predicted) variable*.

Splitting rules can be constructed in many different ways, all of which are based on the notion of *impurity- *a measure of the degree of heterogeneity of the leaf nodes. Put another way, a leaf node that contains a single class is homogeneous and has impurity=0. There are three popular impurity quantification methods: Entropy (aka information gain), Gini Index and Classification Error. Check out this article for a simple explanation of the three methods.

The *rpart* algorithm offers the entropy and Gini index methods as choices. There is a fair amount of fact and opinion on the Web about which method is better. Here are some of the better articles I’ve come across:

https://www.garysieling.com/blog/sklearn-gini-vs-entropy-criteria

http://www.salford-systems.com/resources/whitepapers/114-do-splitting-rules-really-matter

The answer as to which method is the best is: *it depends*. Given this, it may be prudent to try out a couple of methods and pick the one that works best for your problem.

Regardless of the method chosen, the splitting rules partition the decision space (a fancy word for the entire dataset) into *rectangular regions* each of which correspond to a split. Consider the following simple example with two predictors x1 and x2. The first split is at x1=1 (which splits the decision space into two regions x1<1 and x1>1), the second at x2=2, which splits the (x1>1) region into 2 sub-regions, and finally x1=1.5 which splits the (x1>1,x2>2) sub-region further.

It is important to note that the algorithm works by making the* best possible choice at each particular stage, without any consideration of whether those choices remain optimal in future stages*. That is, the algorithm makes *a locally optimal decision *at each stage. It is thus quite possible that such a choice at one stage turns out to be sub-optimal in the overall scheme of things. In other words, the algorithm does not find a globally optimal tree.

Another important point relates to well-known bias-variance tradeoff in machine learning, which in simple terms is a tradeoff between the degree to which a model fits the training data and its predictive accuracy. This refers to the general rule that beyond a point, it is counterproductive to improve the fit of a model to the training data as this increases the likelihood of overfitting. It is easy to see that deep trees are more likely to overfit the data than shallow ones. One obvious way to control such overfitting is to construct shallower trees by stopping the algorithm at an appropriate point based on whether a split significantly improves the fit. Another is to grow a tree unrestricted and then prune it back using an appropriate criterion. The *rpart *algorithm takes the latter approach.

Here is how it works in brief:

Essentially one minimises the *cost*, , a quantity that is a linear combination of the *error* (essentially, the fraction of misclassified instances, or variance in the case of a continuous variable), and the *number of leaf nodes in the tree, *:

First, we note that when , this simply returns the original fully grown tree. As increases, we incur a penalty that is proportional to the number of leaf nodes. This tends to cause the minimum cost to occur for a tree that is a subtree of the original one (since a subtree will have a smaller number of leaf nodes). In practice we vary and pick the value that gives the subtree that results in the smallest cross-validated prediction error. One does not have to worry about programming this because the *rpart* algorithm actually computes the errors for different values of for us. All we need to do is pick the value of the coefficient that gives the lowest cross-validated error. I will illustrate this in detail in the next section.

An implication of their tendency to overfit data is that decision trees tend to be sensitive to relatively minor changes in the training datasets. Indeed, small differences can lead to radically different looking trees. Pruning addresses this to an extent, but does not resolve it completely. A better resolution is offered by the so-called ensemble methods that *average over many differently constructed trees*. I’ll discuss one such method at length in a future post.

Finally, I should also mention that decision trees can be used for both classification and regression problems (i.e. those in which the predicted variable is discrete and continuous respectively). I’ll demonstrate both types of problems in the next two sections.

### Classification trees using rpart

To demonstrate classification trees, we’ll use the *Ionosphere* dataset available in the *mlbench* package in R. I have chosen this dataset because it nicely illustrates the points I wish to make in this post. In general, you will almost always find that algorithms that work fine on classroom datasets do not work so well in the real world…but of course, you know that already!

We begin by setting the working directory, loading the required packages (*rpart* and *mlbench*) and then loading the Ionosphere dataset.

Next we separate the data into training and test sets. We’ll use the former to build the model and the latter to test it. To do this, I use a simple scheme wherein I randomly select 80% of the data for the training set and assign the remainder to the test data set. This is easily done in a single R statement that invokes the uniform distribution (*runif*) and the vectorised function, *ifelse*. Before *invoking* runif, I set a seed integer to my favourite integer in order to ensure reproducibility of results.

In the above, I have also removed the training flag from the training and test datasets.

Next we invoke *rpart*. I strongly recommend you take some time to go through the documentation and understand the parameters and their defaults values. Note that we need to remove the predicted variable from the dataset before passing the latter on to the algorithm, which is why we need to find the column index of the predicted variable (first line below). Also note that we set the *method* parameter to “class*“,* which simply tells the algorithm that the predicted variable is discrete.* *Finally, *rpart* uses Gini rule for splitting by default, and we’ll stick with this option.

The resulting plot is shown in Figure 3 below. It is quite self-explanatory so I won’t dwell on it here.

Next we see how good the model is by seeing how it fares against the test data.

pred true | bad | good |

bad | 17 | 2 |

good | 9 | 43 |

Note that we need to verify the above results by doing multiple runs, each using different training and test sets. I will do this later, after discussing pruning.

Next, we prune the tree using the cost complexity criterion. Basically, the intent is to see if a shallower subtree can give us comparable results. If so, we’d be better of choosing the shallower tree because it reduces the likelihood of overfitting.

As described earlier, we choose the appropriate pruning parameter (aka cost-complexity parameter) by picking the value that results in the lowest prediction error. Note that all relevant computations have already been carried out by R when we built the original tree (the call to *rpart* in the code above). All that remains now is to pick the value of :

CP | nsplit | rel error | xerror | xstd | |

1 | 0.57 | 0 | 1.00 | 1.00 | 0.080178 |

2 | 0.20 | 1 | 0.43 | 0.46 | 0.062002 |

3 | 0.02 | 2 | 0.23 | 0.26 | 0.048565 |

4 | 0.01 | 4 | 0.19 | 0.35 |

It is clear from the above, that the lowest cross-validation error (*xerror* in the table) occurs for (this is CP in the table above). One can find CP programatically like so:

Next, we prune the tree based on this value of CP:

Note that *rpart* will use a default CP value of 0.01 if you don’t specify one in *prune*.

The pruned tree is shown in Figure 4 below.

Let’s see how this tree stacks up against the fully grown one shown in Fig 3.

This seems like an improvement over the unpruned tree, but one swallow does not a summer make. We need to check that this holds up for different training and test sets. This is easily done by creating multiple random partitions of the dataset and checking the efficacy of pruning for each. To do this efficiently, I’ll create a function that takes the training fraction, number of runs (partitions) and the name of the dataset as inputs and outputs the proportion of correct predictions for each run. It also optionally prunes the tree. Here’s the code:

Note that in the above, I have set the default value of the prune_tree to FALSE, so the function will execute the first branch of the if statement unless the default is overridden.

OK, so let’s do 50 runs with and without pruning, and check the mean and variance of the results for both sets of runs.

So we see that there is an improvement of about 3% with pruning. Also, if you were to plot the trees as we did earlier, you would see that this improvement is achieved with shallower trees. Again, I point out that this is not always the case. In fact, it often happens that pruning results in worse predictions, albeit with better reliability – a classic illustration of the bias-variance tradeoff.

### Regression trees using rpart

In the previous section we saw how one can build decision trees for situations in which the predicted variable is discrete. Let’s now look at the case in which the predicted variable is continuous. We’ll use the Boston Housing dataset from the *mlbench* package. Much of the discussion of the earlier section applies here, so I’ll just display the code, explaining only the differences.

Next we invoke *rpart*, noting that the predicted variable is *medv* (median value of owner-occupied homes in $1000 units) and that we need to set the *method * parameter to “*anova*“. The latter tells *rpart* that the predicted variable is continuous (i.e that this is a regression problem).

The plot of the tree is shown in Figure 5 below.

Next, we need to see how good the predictions are. Since the dependent variable is continuous, we cannot compare the predictions directly against the test set. Instead, we calculate the root mean square (RMS) error. To do this, we request *rpart* to output the predictions as a vector – one prediction per record in the test dataset. The RMS error can then easily be calculated by comparing this vector with the *medv* column in the test dataset.

Here is the relevant code:

Again, we need to do multiple runs to check on the reliability of the predictions. However, you already know how to do that so I will leave it to you.

Moving on, we prune the tree using the cost complexity criterion as before. The code is exactly the same as in the classification problem.

The tree is unchanged so I won’t show it here. This means, as far as the cost complexity pruning is concerned, the optimal subtree is the same as the original tree. To confirm this, we’d need to do multiple runs as before – something that I’ve already left as as an exercise for you :). Basically, you’ll need to write a function analogous to the one above, that computes the root mean square error instead of the proportion of correct predictions.

### Wrapping up

This brings us to the end of my introduction to classification and regression trees using R. Unlike some articles on the topic I have attempted to describe each of the steps in detail and provide at least some kind of a rationale for them. I hope you’ve found the description and code snippets useful.

I’ll end by reiterating a couple points I made early in this piece. The nice thing about decision trees is that they are easy to explain to the users of our predictions. This is primarily because they reflect the way we think about how decisions are made in real life – via a set of binary choices based on appropriate criteria. That said, in many practical situations decision trees turn out to be unstable: small changes in the dataset can lead to wildly different trees. It turns out that this limitation can be addressed by building a variety of trees using different starting points and then averaging over them. This is the domain of the so-called random forest algorithm.We’ll make the journey from decision trees to random forests in a future post.

**Postscript, 20th September 2016**: I finally got around to finishing my article on random forests.