The below image shows a numeric outcome being deployed (Regression).

In the context of a regression model being deployed, the performance metrics used to evaluate its effectiveness in production typically include:

Root Mean Square Error (RMSE): This metric provides a measure of the average magnitude of the errors between predicted values by the model and the actual values, giving a sense of how accurately the model predicts the outcome.

Minimum Square Error: While less commonly referenced as "Minimum Square Error", metrics like Mean Squared Error (MSE) are often used to quantify the average of the squares of the errors—essentially, the average squared difference between the estimated values and what is estimated.

These metrics are crucial for assessing the performance of regression models in CRM Analytics, as they directly reflect the accuracy and reliability of the model’s predictions in real-world applications.