Choosing the right error metric is a crucial aspect of evaluating the performance of a machine learning model. The selection depends on the nature of the problem, the characteristics of the data, and the goals of the modeling task. Below, I provide a comprehensive guide on how to choose the right error metric:
1. Regression Problems:
a. Mean Squared Error (MSE):
When to Choose:
- Suitable when the focus is on minimizing large errors.
- Assumes that errors are normally distributed.
When not Chosen:
- Sensitive to outliers, which may distort the evaluation.
- May penalize large errors more heavily.
b. Mean Absolute Error (MAE):
When to Choose:
- Robust to outliers.
- When interpretability is crucial, as it directly represents average error magnitude.
When not Chosen:
- Less emphasis on large errors compared to MSE.
- May result in less smooth predictions.
