# Concepts

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

- [Bayes Error Rate Estimation](BER.md)
- [Cluster Assignment](Clusterer.md)
- [Coverage Analysis](Coverage.md)
- [Detecting Bias in Datasets](Bias.md)
- [Cleaning Datasets](DataCleaning.md)
- [Evaluating Parity](Parity.md)
- [Evaluating Label Parity](LabelParity.md)
- [Dataset Divergence](Divergence.md)
- [Detecting Drift in Datasets](Drift.md)
- [Detecting Duplicates](Duplicates.md)
- [Image Statistics Functions](Stats.md)
- [Outliers Class](Outliers.md)
- [Model Training Techniques](ModelTraining.md)
- [Detecting Out of Distribution Data](OOD.md)
- [Sufficiency of Datasets per Model](Sufficiency.md)
- [Upperbound on Average Precision Estimation](UAP.md)
- [Balance Bias Metrics](Balance.md)
- [Diversity Indices](Diversity.md)


:::{toctree}
:hidden:
:maxdepth: 1

BER.md
Bias.md
Clusterer.md
Coverage.md
DataCleaning.md
Parity.md
LabelParity.md
Divergence.md
Drift.md
Duplicates.md
Stats.md
Outliers.md
ModelTraining.md
OOD.md
Sufficiency.md
UAP.md
Balance.md
Diversity.md
:::

For a quick reference of each algorithm's functionality and requirements, see the Algorithm Overview page.

[Algorithm Overview](metric_table.md)

:::{toctree}
:hidden:
:maxdepth: 1

metric_table.md
:::