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Improving the Score Estimator
Question: A scheduling system scores candidate plans, but an optimization check shows that the plan selected by the search procedure does not score better than the best plan found by exhaustive checking. What part of the system should be improved?
Sample answer: Improve the component that learns or approximates the scoring function, because the score model is failing to rank the truly best plan above the selected plan.
Key points:
- The scoring model or its learning procedure
- The approximation of the score used to rank candidate plans
Rubric: Correctly identifies the scoring model, the learning procedure, or the approximation used to estimate the score.
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Data Science
Foundations of Large Language Models Course
Computing Sciences
D2L
Dive into Deep Learning @ D2L
Machine Learning
Deep Learning
Supervised Learning
Machine Learning Yearning @ DeepLearning.AI
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A machine learning model is being trained for a prediction task. A key metric, the objective function, is tracked over time. The value of this function represents the magnitude of the model's error. A graph of this process shows the objective function's value consistently decreasing as the number of training iterations increases. What is the most accurate interpretation of this trend?
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Surrogate Objective
Loss Function
Differentiable Objectives
Second-Order Optimization Algorithm
Objective Function Curvature
Convex Quadratic Objective Function
Recognizing a Scoring-Function Issue
If a candidate answer gets a lower score than an incorrect answer, the search procedure is usually the part that should be fixed first.
A scoring rule can mislead learning if it gives the correct output a _____ score than the system's prediction.
When to Treat a Scoring Problem as an Objective-Function Issue
When the Scoring Rule Is the Source of the Error
Diagnosing a Scoring-Rule Failure
Automatic Dictation Score Check
Improving the Score Estimator
What an Objective Function Must Do
A Metric Can Still Be Wrong