Answer: The model will not be able to find a unique best-fit solution

## Explanation The correct answer is **B. The model will not be able to find a unique best-fit solution**. This issue arises due to a phenomenon known as the **dummy variable trap**. The dummy variable trap occurs when: 1. **Perfect Multicollinearity**: Including all dummy variables for a categorical feature plus an intercept term creates perfect multicollinearity 2. **Linear Dependence**: The sum of all dummy variables equals 1 (for each observation), which is exactly the same as the intercept term 3. **Matrix Inversion Issues**: This makes the design matrix singular and non-invertible ### Why This Happens: - If you have a categorical variable with k categories, you should only create k-1 dummy variables - Including all k dummy variables plus an intercept creates perfect multicollinearity - The statistical software cannot compute unique parameter estimates ### Consequences: - The model cannot find a unique solution for the coefficients - Standard errors become infinite - Parameter estimates become unstable and unreliable ### Solution: - Drop one dummy variable category (the reference category) - Or remove the intercept term (less common) This issue is particularly important in financial risk modeling where accurate parameter estimation is crucial for default prediction.

Author: Tanishq Prabhu