Data challenges overview

This section focuses on recurring data challenges that strongly influence method selection in real-world machine learning applications.

In practice, choosing a model depends not only on the task, but also on whether the data is limited, noisy, incomplete, dynamic, unstructured, or subject to confidentiality constraints. These challenges often determine which model families are most appropriate.

The main data challenges highlighted in the paper are:

• Small data
• Big data
• Unstructured data
• Noisy data
• Incomplete data
• Dynamic data
• Confidential data

These pages are adapted directly from the paper text.

Data challenges summary