Why can learning-curve values be especially erratic for very small training samples?
Question: Answer in one to three sentences.
Sample answer: With very small samples, random differences in which examples are selected matter a lot. A sample that happens to be unusually clean and representative can produce a better point, while a sample with difficult or mislabeled examples can produce a worse one, so the curve can swing noticeably.
Key points:
- Small samples are highly sensitive to random selection
- Better samples can lower the measured error
- Worse samples can raise the measured error
- This sensitivity causes visible fluctuation at small training sizes
Rubric: The answer should explain that unusually strong or weak small subsets change the measured point and make the curve noisy.
0
1
Tags
Machine Learning
Deep Learning
Supervised Learning
Dive into Deep Learning @ D2L
Data Science
Machine Learning Strategy
Machine Learning Yearning @ DeepLearning.AI
Related
Averaging Errors from Several Small Training Samples
Balanced Small Samples Help Learning Curves Stay Stable on Imbalanced or Multi-Class Data
Why can a learning-curve value jump around when it is measured on a tiny random training sample?
A tiny random training sample can make the measured learning-curve error swing noticeably up or down.
A tiny sample with many unclear or incorrect labels is unusually _____.
Match each small-sample condition to its effect on a learning-curve estimate.
Put the checks in a sensible order for explaining a strange value on a tiny sample.
Why can a training curve wobble when the sample size is very small?
Explain a spike caused by a tiny imbalanced sample.
Why can learning-curve values be especially erratic for very small training samples?
Which situation makes a tiny random sample least likely to represent the full dataset?
A very small random sample is less likely to be misleading when the number of classes is large.