Assessing Regression Model Performance

Requirements Clarification & Assessment

1. Understanding the Purpose:

   • Determine the objective of the regression model. Is it for prediction, explanation, or both?
   • Identify the stakeholders and their expectations regarding model performance metrics.

2. Data Characteristics:

   • Assess the nature of the target variable. Is it continuous, skewed, or contains outliers?
   • Understand the independent variables and their relationship with the target variable.

3. Model Constraints:

   • Are there any computational limitations or time constraints?
   • Are there specific requirements for interpretability or simplicity of the model?

4. Evaluation Metrics:

   • Confirm which performance metrics are most relevant for evaluating the model. Consider metrics like R-squared, Adjusted R-squared, MSE, MAE, RMSE, etc.
   • Determine if there are any domain-specific metrics or thresholds that need to be considered.

5. Overfitting Concerns:

   • Identify the risk of overfitting, especially if the dataset is large with many features.
   • Consider if cross-validation or other techniques are needed to validate model performance.