Model tracing

Model tracing is the practice of monitoring a computational model's internal states and decisions as it runs. It helps researchers and practitioners understand how the model behaves, spot patterns or anomalies, and diagnose issues or errors. Model tracing is especially useful in machine learning, artificial intelligence, and data analysis, where visibility into a model's inner workings helps teams improve performance and reliability. For a practical view of this process, see tracing models and decisions.

Example

A data scientist is working with a machine learning model that classifies emails as spam or not spam. To understand how the model reaches its decisions, the data scientist uses model tracing techniques. By reviewing internal states, feature importance, and the decision process, the data scientist can find biases, flaws, or areas that need improvement. That insight makes it easier to tune the model's parameters, improve accuracy, and deliver a more reliable spam-filtering system.