Enhancing Linear Regression Techniques

Requirements Clarification & Assessment

1. Understand the Objective:

   • Determine the primary goals of optimizing the linear regression model. Is it to improve predictive accuracy, reduce overfitting, or enhance model interpretability?
   • Identify the business or research questions the model aims to address.

2. Data Assessment:

   • Review the dataset to understand its structure, including the number of features, data types, and the presence of missing values or outliers.
   • Evaluate the quality and relevance of each feature in relation to the target variable.

3. Assumption Verification:

   • Ensure that the fundamental assumptions of linear regression (linearity, independence, homoscedasticity, normality of residuals, and lack of multicollinearity) are met.
   • Identify any violations of these assumptions that may need to be addressed.

4. Model Evaluation Metrics:

   • Define the metrics for model evaluation, such as Mean Squared Error (MSE), R-squared, or Root Mean Squared Error (RMSE).
   • Determine the acceptable thresholds for these metrics based on the context of the problem.

5. Constraints and Resources:

   • Identify any constraints, such as computational resources, time limitations, or data availability, that may impact the model optimization process.