Why Skip Data That Does Not Match the Evaluation Set?

According to machine learning strategy, if the dev error curve has _____, adding more training data is unlikely to help you reach your target.

Why might you exclude data that does not add useful information for training?

True or False: More training data always improves validation accuracy.

If the dev error curve has _____, adding more training data is unlikely to move you toward the target performance.

Match each machine-learning concept with the description that fits it best when deciding whether collecting more data is worthwhile.

Order the steps for using a learning curve to judge whether collecting more training data is worthwhile.

In a leaf-disease classifier, why should a large archive of handwritten invoices be left out of training?

True or False: Examining the learning curve can help you avoid spending months collecting more data that later turns out not to improve validation performance.

When compute is limited, examples that add no _____ should be left out of training.

Match each data scenario to the best action.

Order the steps for deciding whether to add a new data source to training.

Why Irrelevant Training Data Should Be Excluded

Whether to Add Contract Scans to a Plant Photo Classifier

Irrelevant Training Data and Model Capacity