This sounds like a warmup question until they ask you to actually derive things.
Start by explaining why logistic regression is naturally suited for binary classification, focusing on its probabilistic output and decision boundary. Then justify the probabilistic interpretation by linking the logistic function to the log-odds and maximum likelihood estimation. Conclude by discussing practical implications and trade-offs.
Pro tip: Mention that logistic regression outputs well-calibrated probabilities by default, but you can further improve calibration with techniques like Platt scaling if needed—this shows awareness of real-world deployment concerns.
State that binary classification requires predicting a probability between 0 and 1, and logistic regression directly models this.
Describe how the logistic (sigmoid) function maps any real-valued linear combination to (0,1), ensuring valid probability outputs.
Connect the output to the log-odds: log(p/(1-p)) = w·x + b, and explain that maximum likelihood estimation yields probabilities that reflect true class frequencies.
Mention that a threshold (e.g., 0.5) converts probabilities to class labels, and that the threshold can be tuned based on business needs.
Note that logistic regression assumes linearity in log-odds, which may be limiting, but its interpretability and probabilistic nature are advantages.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.