MML-14-07: Logistic Regression: Odds, Logit, Sigmoid & Binary Cross-Entropy – Practice Quiz

40 worked single-select and multiselect questions on Logistic Regression: Odds, Logit, Sigmoid & Binary Cross-Entropy. Cognitive level: Tier 2–3. Prerequisites: Parts 06, 11.

Learning objectives: Convert between probabilities, odds and logits; Interpret conditional coefficients and feature transformations; Compute Bernoulli likelihood and binary cross-entropy; Derive and evaluate probability and loss gradients; Diagnose saturation, weighting and misspecification; Separate probability estimates from hard decisions.

Each item is self-contained, with calculations, model diagnostics and explanations of every distractor. Study references: Stanford CS229 notes.