Loss Functions, Behaviors & Selection Questions
Loss function design, evaluation, and selection in machine learning. Includes common loss functions (MSE, cross-entropy, hinge, focal loss), how loss properties affect optimization and gradient flow, issues like class imbalance and label noise, calibration, and practical guidance for choosing the most appropriate loss for a given task and model.
HardTechnical
0 practiced
Design a loss-level approach to enforce equalized odds across demographic groups in a classifier. Describe converting fairness constraints into Lagrangian-penalty terms, discuss optimization challenges from non-convexity, and propose monitoring, rollback, and stakeholder communication strategies for production deployments where fairness and accuracy trade-offs must be justified.
MediumSystem Design
0 practiced
Given a large image classification dataset suspected to contain noisy labels, outline a practical pipeline to detect and mitigate label noise at scale. Include steps for: training schedule to surface noisy examples, loss-based noisy sample identification (small-loss trick), ensembling predictions, robust loss selection, human-in-the-loop relabeling, and operational considerations for iterative retraining in production.
HardTechnical
0 practiced
Implement a differentiable surrogate loss that approximates macro-F1 for multi-class classification in PyTorch. Outline a soft confusion-matrix approach using predicted probabilities to compute soft precision and recall per class, then a soft F1, implement the forward pass vectorized, and discuss limitations such as smoothing bias and weak gradients for rare classes.
HardSystem Design
0 practiced
Design a production monitoring system that tracks per-class and per-cohort loss distributions over time, detects concept drift, and triggers retraining pipelines. Specify data retention policies, statistical tests (e.g., KS, population stability index), thresholds for alerts, and strategies for handling low-volume slices and class imbalance when computing meaningful loss statistics.
EasyTechnical
0 practiced
Define probability calibration and describe common calibration metrics (Expected Calibration Error, reliability diagrams). Explain simple post-hoc calibration methods such as temperature scaling and Platt scaling, and describe when and how you would apply calibration in a production ML pipeline.
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