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.