Explanation¶

Functional Overview¶

For a quick reference of each algorithm’s task specific functionality and input requirements, see the Functional Overview page.

Supported Roles¶

For a more in-depth understanding of how DataEval supports common roles found in machine learning and AI development, see our Supported Roles page.

Concepts¶

These explanations dive into the what, why and theory behind each of DataEval’s features.

• Balance

• Bayes Error Rate

• Clustering

• Coverage

• Completeness

• Dataset Balancing

• Dataset Cleaning

• Dataset Prioritization

• Divergence

• Diversity

• Drift

• Embeddings

• Image Statistical Analysis

• Label Parity

• Metadata

• Out-of-Distribution Data

• Outliers

• Parity

• Sufficiency of Data

• Upperbound on Average Precision