Diagnosing a team's learning curve trend
Case context: A machine learning team has plotted a learning curve for their dev-set error. They started with 500 examples and observed a dev-set error of 25%. They subsequently increased the training set to 2,000 examples, then 5,000 examples, and are analyzing the resulting dev-set errors.
Question: Based on the fundamental relationship described in Machine Learning Yearning, what trend must the team observe in the dev-set error as they evaluate the model trained on 2,000 and 5,000 examples compared to the initial 500?
Sample answer: The team should observe a decreasing trend in the dev-set error. As they move from 500 to 2,000 and then to 5,000 training examples, the dev-set error should steadily drop from the initial 25%.
Key points:
- The dev-set error should decrease.
- This expected decrease occurs as the training set size increases.
Rubric: The student must correctly identify that the dev-set error is expected to decrease as the training set size grows from 500 to 5,000 examples.
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Machine Learning
Deep Learning
Supervised Learning
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Related
According to Machine Learning Yearning, what happens to dev-set error as training set size increases?
True or False: As the training set size increases, the dev-set error is expected to decrease.
As training-set size increases, dev-set error should _____.
Match each learning curve concept to its correct description.
Order the steps to generate and interpret a dev-set learning curve.
A team trains on 1,000 examples and gets 18% dev-set error. After adding 9,000 more training examples, what should they expect?
True or False: Rising dev-set error as training set size grows is the normal expected behavior in Machine Learning Yearning.
The learning curve for dev-set error plots _____ on the y-axis against training set size on the x-axis.
Match each observed learning curve behavior to its correct interpretation.
Order the reasoning steps for deciding whether to collect more training data based on the dev-set learning curve.
Explain the relationship between training set size and dev-set error.
Diagnosing a team's learning curve trend
Expected trend of dev-set error