Concept icon
Concept

Comparing Errors Across Two Data Sources

A diagnostic error table can help you compare a model on two different data sources, such as recordings from quiet rooms and recordings from noisy rooms. Put the two sources across the top of the table and use three rows for human-level error, error on examples used for training, and error on examples the model never saw during training. This layout makes it easier to tell whether a problem comes from one source, from the training data, or from generalization. Filling in more entries can sometimes uncover patterns that are hard to notice from a single summary number.

0

1

Concept icon
Updated 2026-08-12

Contributors are:

Who are from:

Tags

Machine Learning

Deep Learning

Supervised Learning

Dive into Deep Learning @ D2L

Data Science

Machine Learning Strategy

Machine Learning Yearning @ DeepLearning.AI