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Interpreting Training Metrics
A machine learning practitioner is debugging a training process. They notice that the error value printed after every single data point fluctuates wildly, but the error value printed at the end of each complete pass through the dataset is steadily decreasing. Explain why these two error values behave differently, using the correct terminology for each.
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Ch.2 Generative Models - Foundations of Large Language Models
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An engineer is training a model on a large dataset. They are monitoring two metrics:
- Metric A: A value calculated for each individual data sample. This value fluctuates significantly from one sample to the next.
- Metric B: A single, aggregate value calculated after the model has processed the entire training dataset. This value shows a steady, downward trend over multiple passes through the dataset.
Based on the standard terminology for measuring a model's performance, what is the most accurate way to classify these two metrics?
Interpreting Training Metrics
Match each term to its most accurate description regarding how a model's performance is measured during training.