In a fraud-detection learning curve, why might a class-balanced subsample be better than a purely random subsample when the data are very skewed?

True or False: On highly imbalanced data with many labels, drawing ordinary random subsets usually makes learning curves less noisy than building subsets that preserve the class mix.

When you downsample a dataset with many classes and uneven frequencies, a _____ subset preserves the class mix from the original data as closely as practical.

When is a balanced subset useful for learning-curve plots?

A class-balanced subset for a learning curve should keep class proportions close to those in the full training set.

Reducing curve noise with a representative sample

Match each sampling idea with its description.

Put the steps in order for building one class-balanced sample used to estimate a point on a learning curve.

Why can balanced subsets be useful when drawing learning curves for highly skewed or many-class data?

True or False: Randomly drawing very small training subsets always gives smooth learning curves, even when some classes are rare.

In a 12-example subset that keeps the same 25% positive rate as the full data, you should include _____ positive examples.

Match each data setup to the sampling issue it creates in small learning-curve subsets.

Order the steps for deciding whether to use stratified subsets when drawing learning-curve data.

Why do skewed class distributions make learning curves noisy, and how do balanced subsets help?

Explain why the learning curve is jagged for a skewed classifier and choose an appropriate subset strategy.

How should class proportions be chosen for balanced learning-curve samples?