> For the complete documentation index, see [llms.txt](https://docs-old.evidentlyai.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs-old.evidentlyai.com/presets/all-presets.md).
# All Presets
An overview of the evaluations you can do with Evidently.
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**You are looking at the old Evidently documentation**. Check the newer version [here](https://docs.evidentlyai.com/introduction).
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Evidently has several pre-built reports and test suites. We call them **Presets**. Each preset evaluates or tests a particular aspect of the data or model quality.
This page links to the **description** of each preset. To see the code and interactive examples, head to [example notebooks](/tutorials-and-examples/examples.md) instead.
## Metric Presets
Metric presets are **pre-built reports** that help with visual exploration, debugging and documentation of the data and model performance. You can also use them to calculate and log metrics as JSON or Python dictionary.
| | | |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Data Quality
Shows the dataset statistics and feature behavior.
Requirements: model inputs.
| Data Drift
Explores the distribution shift in the model features.
Requirements: model inputs, a reference dataset.
| Target Drift
Explores the distribution shift in the model predictions.
Requirements: model predictions and/or target, a reference dataset.
|
| Classification
Evaluates the classification model quality and errors.
Requirements: model predictions and true labels.
| Regression
Evaluates the regression model quality and errors.
Requirements: model predictions and actuals.
| Text Overview
Evaluates text data drift and descriptive statistics.
Requirements: model inputs (raw text data)
|
## Test Presets
Test presets are **pre-built test suites** that perform structured data and model checks as part of the pipeline.
| | | |
| ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| NoTargetPerformance
Tests the model performance without ground truth or actuals.
Requirements: model inputs, predictions, a reference dataset.
| Data Quality
Tests if the data quality is suitable for (re)training. Checks nulls, duplicates, etc.
Requirements: model inputs.
| Data Stability
Tests if a data batch is similar to reference. Checks schema, data ranges, etc.
Requirements: inputs, a reference dataset.
|
| Data Drift
Tests for distribution drift per column and overall dataset drift.
Requirements: model inputs, a reference dataset.
| Regression
Tests the performance of the regression model against expectation.
Requirements: model predictions and actuals.
| Multi-class Classification
Tests the performance of a multi-class classification model against expectation.
Requirements: model predictions, true labels.
|
| Binary Classification
Tests the performance of a binary classification model against expectation.
Requirements: model predictions, true labels.
| Binary Classification top-K
Tests the performance of a binary classification model at top-K.
Requirements: model predictions, true labels.
| |
## Individual Metrics and Tests
You can also create custom test suites and reports from individual metrics and tests. You can explore 100+ [available tests](/reference/all-tests.md) and [metrics](/reference/all-metrics.md).