Machine Learning in Practice

Machine Learning in Practice

Project failures in IT are all too common. The risks are higher if you are adopting a new technology that is unfamiliar to your organisation. Machine learning has been around for a long time in academia, but awareness and development of the technology has only recently reached a point at which its benefits are becoming attractive to business. There is huge potential to reduce costs and find new revenue by applying this technology correctly, but there are also pitfalls.

This guide will help you apply machine learning effectively to solve practical problems within your organisation. I'll talk about issues that I've encountered applying machine learning in industry. My experience is in applying machine learning to analysis of text, however I believe the lessons I have learnt are generally applicable. I have been able to deliver significant and measurable benefits through applying machine learning, and I hope that I can enable you to do the same.

I will assume that you know the basics of machine learning, and that you have a real-world problem that you want to apply it to. This is not an introduction to machine learning (there are already plenty of those), however I don't assume that you're a machine learning expert. A lot of the advice is non-technical and would be just as useful to a product manager wanting to understand the technology as a software developer creating a solution.

Clearly understand the business need

Understanding the business need is important for any project, but it is easy to get blinded by technological possibilities. Is machine learning really going to benefit the company, or is it possible to achieve the same goals (or most of them) with some simple rules? The goal is to build a solution, not to do machine learning for the sake of it.

Try and identify all the metrics that are important to the business. The metrics we are optimising for have a profound effect on the solution we choose, so it is important to identify these early on. It also affects what alternatives there are to machine learning.

In the case of classification problems, potential metrics to consider are

• accuracy: the proportion of all instances classified correctly. Note that this can be very misleading if the data is biased (if 90% of the data is from class 1, we can get 90% accuracy by simply classifying everying as being from that class). Real data is normally biased in some way. For this reason, you may want to consider an average of the accuracy on each class, or some other measure.
• precision is needed when the results need to look good, for example if they are being presented to customers without any manual filtering after the machine learning phase.
• high recall is important when combining machine learning with manual analysis to produce a combined system with high overall accuracy.
• F1 score, or more generally Fβ score is useful when a trade-off between precision and recall is needed, and β can be adjusted to prefer one over the other.

Customer Service at Direct Electric

Direct Electric are a large electricity company based in the south of England. Dave, the head of customer service, is concerned about response times for upset customers who contact the company online. He wants to ensure that if a customer sends an angry email, a representative will get back to them quickly.

“At the moment, it takes about two days to respond, and I'd like to get that down to half a day,” he explains to Samantha, the resident machine learning expert on the software development team. Dave has heard about automated sentiment analysis, and wonders if that could be used to quickly identify the emails of interest, so that they can be prioritised by the customer service team.

“What we could do,” suggests Samantha, “is try and identify the emails that are likely to carry negative sentiment automatically, and send those to your team to look at first.”

“That sounds good!”

“The thing is,” says Samantha, “A machine-learning based system isn't going to get everything right. Would it matter if we missed some of the negative sentiment emails?” Samantha thinks a high precision system may be what they are looking for. In this case, we will most likely have to sacrifice recall, and miss some of the emails of interest.

“Well, not really,” says Dave, “it's only really useful to us if it finds them all.”

“Well, if you want to guarantee you find all of them,” says Samantha, “the only way to do that is to examine them manually.” Dave looks crestfallen. “But,” she continues, “we could probably get nearly all of them. Would it matter if we accidentally prioritised some articles that aren't really negative?” She is thinking of trying to build a system with high recall, which will probably mean lower precision.

“That would be fine,” says Dave. “After all, at the moment, we're reading them all.”

Sign up below to read all seven chapters:

1. Clearly understand the business need

2. Know what's possible

3. Know the data

4. Plan for change

5. Avoid premature optimisation

6. Mitigate risks

7. Use common sense

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