As part of our interview cycle, candidates work with some data and build a simple model. After we talk through the modeling and data work, I ask them to come up with a business case for the model. Once they have done so, I follow up with:
How would you measure the success of this model in production?
I have heard a lot of answers. They generally fall into two categories: machine learning theory focused, and business-focused. The first is good, the second is better. I will go through each below.
To have something concrete to discuss, we will consider the following problem: “Train a model to classify customers as ‘high-value customer’, and use it to decide if we are going to show them an up-sell page.”
Machine Learning Theory Focused
A common answer, especially from more junior interviewees, is “Accuracy”, which is the number of things your model classifies correctly divided by the total number of things.
Accuracy is rarely a good answer to real-world problems because the classes are often imbalanced. If only 1 in 1000 users is a ‘high-value customer’, then a model that only returns ‘false’, despite having an accuracy of 99.9%, would be clearly worthless.
When pushed about accuracy’s obvious shortcomings, the candidate may fall back to something like F1 score, which is the harmonic mean of precision and recall. F1 is a lazy answer. It is better than accuracy because it is relatively less sensitive to imbalanced data, but rarely are precision and recall equally important (how the customer interacts with the model determines their relative weighting) and so the harmonic mean is not often applicable.
The best candidates consider the problem more deeply. Instead of jumping to find a metric, they start by thinking about the experience of using a model from a business or user perspective. A good way to frame this is “What does a false positive cost my user?” and “How does that compare to the cost of a false negative?”
Sometimes a false positive is costly, as might be the case if the resulting action is drastic, like shutting down a user’s account. To avoid that, we need to be confident when we take action that we are only targeting the right users. In such a scenario, precision is more important since we want to make sure that most of the events the model flags are true positives. This is not the situation for our ‘high-value customer’ model, because showing a user an up-sell page is unlikely to hurt them or cause them to churn.
Instead in our case, a false negative is costly, because we lose the chance at a large revenue increase from the up-sell. Recall is more important, as we would rather show a few extra users our up-sell page than miss the chance to convert a sale.
There are many other metrics that might be useful. The important part is not so much the metric itself, but what is motivating it, which should be the business use case and customer experience.
A Great Metric: Dollars
A great metric is the formalized version of our customer-focused one: dollars. Assigning a dollar value to each model result (true positive, false positive, etc.) would allow us to optimize for revenue. This is often feasible in simple models (like our ‘high-value customer’ model), but in more complicated ones (like the fraud models I work on) it can be difficult (One of our largest costs is a reputational risk, which is very hard to assign a number to). For an example of using dollars in model optimization, see Airbnb’s post on Fighting Financial Fraud with Targeted Friction.
In short, a good metric is one which ties closely to the business or customer use. Consider their point of view when answering a metrics question.