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Said no pretty quickly, which was right, but then stumbled trying to explain why in a clean way.
Start by clarifying that MSE is not typically appropriate for logistic regression because it leads to non-convex optimization and poor probabilistic calibration. Explain the theoretical and practical reasons, then contrast with the standard cross-entropy loss, highlighting why it is preferred.
Pro tip: Mention that while MSE can technically be used with logistic regression, it often results in slow convergence and suboptimal probability estimates, which is why cross-entropy is the industry standard.
Briefly explain what MSE and logistic regression are, and why the choice of cost function matters for model training.
Explain that MSE with logistic regression leads to a non-convex cost surface, making optimization prone to local minima and slow convergence.
Mention that MSE does not align with the probabilistic nature of logistic regression, often resulting in poorly calibrated probabilities and reduced classification performance.
Describe cross-entropy (log loss) as the appropriate cost function, emphasizing its convexity and maximum likelihood foundation.
Summarize that MSE is generally not recommended for logistic regression, and cross-entropy should be used for better optimization and probabilistic outputs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.