Derive and implement calibration via temperature scaling
Company: NewsBreak
Role: Machine Learning Engineer
Category: Machine Learning
Difficulty: medium
Interview Round: Technical Screen
Given a trained softmax classifier with logits z, derive temperature scaling for probability calibration: define p_i(x; T) = softmax(z_i(x) / T). Formulate the negative log-likelihood on a held-out validation set and derive the gradient with respect to T. Then write Python code to learn T by minimizing this objective (e.g., with gradient descent) and a function that applies the learned T to calibrate new predictions.
Quick Answer: This Machine Learning question evaluates understanding of model calibration and probabilistic outputs via temperature scaling, requiring formulation of the negative log-likelihood, analytic gradient derivation with respect to a scalar temperature, and coding an optimization to learn that parameter.