Evaluating a Training Update
A data scientist is training a model. After a single training update, the model's predicted probabilities for the correct outputs on two specific examples from the training dataset have changed as shown below. Based on the training objective of maximizing the sum of conditional log-likelihoods, did this specific update improve the model with respect to these two examples? Justify your answer with a calculation.
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A model is being trained by maximizing the sum of log-probabilities for a dataset of 1,000 examples. Consider two scenarios for a single training update:
Scenario A: The probability assigned to the correct output for one example improves from 0.1 to 0.2. The probabilities for all other 999 examples remain unchanged.
Scenario B: The probability assigned to the correct output for one example improves from 0.8 to 0.9. The probabilities for all other 999 examples remain unchanged.
Which scenario leads to a larger increase in the overall training objective function, and why?
Model Comparison using Conditional Log-Likelihood
Evaluating a Training Update