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Requirements Clarification & Assessment
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.
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.
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.
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.
Constraints and Resources:
Identify any constraints, such as computational resources, time limitations, or data availability, that may impact the model optimization process.