Model assertion
We can use traditional programming assertion in machine learning modeling to ensure that the model is behaving as expected. Model assertions can help us detect issues early on, such as input data drift or other unexpected behaviors that might affect the model’s performance. We can consider model assertions as a set of rules that get checked during the model’s training, validation, or even during deployment to ensure that the model’s predictions meet the predefined conditions. Model assertions can help us in many ways, such as detecting issues with the model or input data, allowing us to address them before they impact the model’s performance. They can also help us maintain the model’s performance. Here are two examples of model assertions:
- Input data assertions: These can check that the input features fall within an expected range or have the correct data type. For example, if a model predicts house prices based on the number...
