How can you improve your ML performance?


 Min Read

Low accuracy in machine learning can lead to serious consequences. Model bias and data leakage in machine learning are prime examples of what can occur when a machine learning (ML) model is not properly trained and model monitoring isn’t performed.

Take the COMPAS case, for example. The Correctional Offender Management Profiling for Alternative Sanctions, or COMPAS AI system, was programmed to assess the risk for recidivism of different offenders. But there was a problem, the COMPAS model’s algorithm was inherently biased against people of color. In the end, the COMPAS system predicted double the number of false positives for recidivism for Black Americans compared to White Americans. This resulted in extremely inaccurate risk assessment scores which were presented to judges during criminal sentencing. Naturally, the falsely-inflated risk assessment scores generated by the COMPAS algorithm caused many offenders to receive unjust sentences. 

This is just one example of how dangerous AI can be when a model does not perform correctly. If AI is to reach its full potential, greater care must be taken to monitor model performance. But how can you improve the performance of a ML model? Optimizing Machine Learning Operations (MLOps) using an AI Observability platform allows developers to continuously monitor and improve model performance in machine learning. In short, AI observability takes MLOps and model monitoring tools to the next level. 

In this article we’ll briefly outline how model performance is currently evaluated and explain how an AI observability platform can successfully improve performance and reduce the risk of harmful AI errors. 

Evaluating performance of a model in machine learning

Currently ML models are evaluated using a myriad of  machine learning performance metrics. Here are just a few examples of the most popular machine learning metrics used to evaluate performance:

Regression metrics

Regression metrics are essential quantitative measures used to evaluate the performance and accuracy of a regression model's predictions. For example, the Mean Squared Error (MSE) metric is one of the most popular regression metrics used in ML modeling. The MSE metric is by taking the average square differences between the model’s predicted values and actual values. Other regression metrics include Mean Absolute Error (MAE) and R-squared.

Classification metrics

Classification metrics, such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic (ROC) curve, are essential measures used to evaluate the performance and effectiveness of a classification model's predictions on labeled data.. A prime example of a classification metric is the F1-score. The F1-score is a classification metric that combines precision and recall into a single value, providing a balanced measure of a model's performance, especially in cases of imbalanced datasets. Precision is the proportion of true positive predictions out of all positive predictions, while recall is the proportion of true positive predictions out of all actual positive instances. The F1-score is the harmonic mean of precision and recall, calculated as:

$$F_{1}  = 2 \frac{precision * recall}{precision + recall}$$

By using the harmonic mean, the F1-score gives more weight to lower values of precision and recall, thus ensuring that both are considered equally important in evaluating a model's performance. A higher F1-score indicates better performance, with a maximum value of 1 representing perfect precision and recall.

The more false positives and negatives that occur, the lower the F1-score of a model.

Metrics are critical to understanding how to improve the overall performance. However, even with a strongly performing model bias and other issues can begin to creep in. So, what can be done? Let’s see how an AI observability platform can solve this problem.

Discover how to increase performance of machine learning models with an AI observability platform

An AI Observability platform acts as a control system at the core of the MLOps lifecycle. Because of frequent data changes, the performance of a machine learning model can fluctuate over time. This means that no one will know exactly how a model will perform until it is deployed and tested against real life scenarios. That’s why constant ML monitoring is needed to ensure that a model is performing as expected on an ongoing basis. Here is a visual representation of what an AI observability platform looks like in practice: 

Fiddler MPM lifecycle

As an example, let’s explore how an AI observability platform can be used to overcome the challenge of model bias.

Model bias

Since models are trained using existing data, they have the potential to propagate existing bias or even introduce new bias. But with an AI observability platform, model bias can be detected and eradicated before any harm is done. An AI observability platform can explain where issues are arising and trigger alerts that are shared with all stakeholders, thus improving performance and increasing transparency. 

An AI observability platform allows ML teams to augment their traditional monitoring processes with explainable AI, providing actionable, real-time insights into ML performance. In short, machine learning is no longer a black box. 

Interested in seeing how Fiddler can help you maintain a high-performance model? Try Fiddler for free today!