To many, artificial intelligence (AI) and machine learning (ML) represent truly huge leaps for mankind, transforming a wide range of industries and processes at the heart of human experience. From how we engineer software programs to how we communicate with each other, discover entertainment, and more, there’s no reason to expect these innovations to slow down any time soon.
In business settings, the applications have proven to be virtually endless, enabling companies to know, understand, and serve their customers better. Every day, the average person generates a wealth of data, especially if they spend much time online. This means even greater possibilities for personalized services and improved customer relationships, but all of this progress does raise a key question:
How do we know these machine learning models are actually trustworthy?
The algorithms and automations at the heart of these processes are meant to replicate human intelligence and thinking patterns, which can, of course, go off the rails once in a while. To a certain extent, this is inevitable since the ML models applied to raw data act like a living entity — and new data, or new forms of data, can challenge established models. When so much of AI is essentially a “black box,” the ever-evolving nature of ML models can be tough to keep pace with.
In this article, we’re going to focus on how to assess and improve machine learning models’ accuracy and performance. What is machine learning performance, and what role does accuracy play? How should we intervene when we’re able to perceive bias or inaccuracies in a machine learning model? Read on for the answers.
While machine learning models can be developed and put into production relatively quickly, it’s important to make sure that they’re not rolled out without proper evaluation and refinement. This is similar to the construction adage of measuring twice and cutting once, making sure everything is precise before taking the next action.
When discussing and evaluating model performance in machine learning, there are some key terms to define and differentiate — primarily a model’s accuracy and performance.
If you’re wondering which is more important, model accuracy or model performance, the answer is simpler than you might think, as model accuracy is only one subset of performance. With that in mind, it’s worth defining model accuracy vs model performance:
Depending on the application, ML model performance metrics might include evaluating its accuracy (in classifying a data set, for example) as well as the model’s real-time performance and adaptability to new information and other performance markers.
As its name suggests, an ML model’s accuracy relates to its percentage of predictions generated by the model that turned out to be accurate. Any basic model accuracy formula you might choose provides developers with high-level insight to better understand the precision of their models. This way, accuracy issues can be quickly detected, assessed, and mitigated.
Model accuracy is important to evaluate and monitor over time because it helps gauge the model’s performance, including its ability to process, understand, and even forecast future events or outcomes. Especially as new data enters the ecosystem, continuing to monitor the model’s accuracy helps prevent problems (like model bias, which we’ll get to in a bit) from impacting, if not outright hijacking, its performance and reliability.
As a vital component of ML performance, model accuracy is definitely a measure to keep a close eye on. In a perfect world, an ML algorithm might be a “set it and forget it” kind of thing, but the real world is much more complex and ever-changing, so models must be constantly evaluated and re-evaluated to ensure they’re performing as intended.
It’s difficult to overstate the importance of continuous ML model monitoring. The original dataset a model is trained on is not guaranteed to maintain its accuracy when new and varied data enters the picture. Then, it becomes important to not only gauge how accurately the model adapts, but also how well it continues to perform over time.
At a high level, implementing an ML monitoring framework helps ensure that, in production, models are accurate and high-performing. Model monitoring can and should occur throughout an ML model’s lifespan. This helps ensure ongoing accuracy and performance when the model ingests real-world data.
At its core, all machine learning models operate based on assumptions, but the reliance on assumptions alone does not denote bias. The real problem arises when the model was trained on a biased dataset or exhibits biased predictions. But model bias can be quite difficult to identify, especially as a result of intersectional unfairness.
All of this underscores the importance of monitoring ML models. Next, we’ll answer questions like “What is model performance?” and discuss how to evaluate model performance from a strategic perspective.
Any predictive model performance evaluation should focus on a few key areas to ensure there are no blind spots within the model, such as:
It’s natural for ML models to degrade — or experience diminished accuracy — over time. This might occur as either a result of changes being made on the backend, changes to the data the model is ingesting, or in response to data inputs that the model wasn’t sufficiently trained for.
Data drift refers to any variation in the distribution of model data, whether that involves feature drift in the model features that feed predictions or label drift in the outputs of the model. Several different factors can cause or contribute to data drift, including the simple passage of time. Errors in data collection and model governance may also cause significant drift in some cases.
Measuring and understanding an ML model’s performance is the first step toward improving and optimizing the model, either before it goes into production or after it is deployed. Much like model accuracy, model performance is most useful when it’s monitored over time, as an ongoing process. Keeping an eye on the model’s performance over time helps ensure that problems can be identified and remedied in a timely manner, future strategies can be developed based on high-quality predictive data, and issues within the model can be debugged through efficient and effective processes.
Understanding how reliable and comprehensive the data that went into the model — and monitoring how reliably it can be expected to process incoming and future data — falls into the data integrity category. Throughout an ML model’s lifecycle, keeping a close eye on its accuracy and ability to continue making accurate predictions over time are crucial considerations.
Maintaining data integrity can be tricky, but it’s also inevitable that an ML model will need to be monitored and adjusted over time. It’s a reflection of how complex these models can be, and typically are. They rely on sophisticated programming, data pipelines, automations, and workflows. And raw data is rarely neat and tidy, meaning this data might go through a few transformations in order to be usable within the ML framework. Like translating a sentence from one language to another, at each transformation stage there is a risk of some precise detail being lost in translation, or misapplied.
Ultimately, the sooner issues with data integrity can be identified and mitigated, the more quickly an in-production model can be altered and updated in response.
We saved one of the most important machine learning evaluation metrics for the end of our list. Bias can exist in many shapes and forms, and can be introduced at any stage in the model development pipeline. At a fundamental level, bias is inherently present in the world around us and encoded into our society. We can’t directly solve the bias in the world. On the other hand, we can take measures to weed out bias from our data, our model, and our human review process. The priority from a model performance and monitoring perspective is simple: identify the issue’s root cause — and the extent to which it is currently impacting the model’s accuracy or overall performance — and make the necessary adjustments to mitigate future issues.
The concept of explainable AI means you can better understand the how and why of your ML models before and during production. With advanced model monitoring features, you can quickly detect, assess, and mitigate model issues at an enterprise scale. Not only that, but you can identify problems more quickly, develop effective solutions, and monitor their impact. Our proprietary Bias Detector tool empowers teams with the know-how they need to detect, assess, and mitigate bias within their machine learning models.
Our AI Observability platform helps teams to better manage increasingly complex ML models through continuous model monitoring and explainable AI.
Try Fiddler for free today to see how Fiddler can improve your ML model performance.
