> For the complete documentation index, see [llms.txt](https://docs.seldon.ai/alibi-detect/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.seldon.ai/alibi-detect/algorithms.md).

# Algorithm Overview

The following tables summarize the advised use cases for the current algorithms. Please consult the method specific pages for a more detailed breakdown of each method. The column *Feature Level* indicates whether the detection can be done and returned at the feature level, e.g. per pixel for an image.

## Outlier Detection

| Detector                                                                                                          | Tabular | Image | Time Series | Text | Categorical Features | Online | Feature Level |
| ----------------------------------------------------------------------------------------------------------------- | :-----: | :---: | :---------: | :--: | :------------------: | :----: | :-----------: |
| [Isolation Forest](https://github.com/SeldonIO/alibi-detect/blob/master/docs-gb/od/methods/iforest.ipynb)         |    ✔    |       |             |      |           ✔          |        |               |
| [Mahalanobis Distance](https://github.com/SeldonIO/alibi-detect/blob/master/docs-gb/od/methods/mahalanobis.ipynb) |    ✔    |       |             |      |           ✔          |    ✔   |               |
| [AE](https://github.com/SeldonIO/alibi-detect/blob/master/docs-gb/od/methods/ae.ipynb)                            |    ✔    |   ✔   |             |      |                      |        |       ✔       |
| [VAE](https://github.com/SeldonIO/alibi-detect/blob/master/docs-gb/od/methods/vae.ipynb)                          |    ✔    |   ✔   |             |      |                      |        |       ✔       |
| [AEGMM](https://github.com/SeldonIO/alibi-detect/blob/master/docs-gb/od/methods/aegmm.ipynb)                      |    ✔    |   ✔   |             |      |                      |        |               |
| [VAEGMM](https://github.com/SeldonIO/alibi-detect/blob/master/docs-gb/od/methods/vaegmm.ipynb)                    |    ✔    |   ✔   |             |      |                      |        |               |
| [Likelihood Ratios](https://github.com/SeldonIO/alibi-detect/blob/master/docs-gb/od/methods/llr.ipynb)            |    ✔    |   ✔   |      ✔      |      |           ✔          |        |       ✔       |
| [Prophet](https://github.com/SeldonIO/alibi-detect/blob/master/docs-gb/od/methods/prophet.ipynb)                  |         |       |      ✔      |      |                      |        |               |
| [Spectral Residual](https://github.com/SeldonIO/alibi-detect/blob/master/docs-gb/od/methods/sr.ipynb)             |         |       |      ✔      |      |                      |    ✔   |       ✔       |
| [Seq2Seq](https://github.com/SeldonIO/alibi-detect/blob/master/docs-gb/od/methods/seq2seq.ipynb)                  |         |       |      ✔      |      |                      |        |       ✔       |

## Adversarial Detection

| Detector                                                                                                              | Tabular | Image | Time Series | Text | Categorical Features | Online | Feature Level |
| --------------------------------------------------------------------------------------------------------------------- | :-----: | :---: | :---------: | :--: | :------------------: | :----: | :-----------: |
| [Adversarial AE](https://github.com/SeldonIO/alibi-detect/blob/master/docs-gb/ad/methods/adversarialae.ipynb)         |    ✔    |   ✔   |             |      |                      |        |               |
| [Model distillation](https://github.com/SeldonIO/alibi-detect/blob/master/docs-gb/ad/methods/modeldistillation.ipynb) |    ✔    |   ✔   |      ✔      |   ✔  |           ✔          |        |               |

## Drift Detection

| Detector                                                                                                                    | Tabular | Image | Time Series | Text | Categorical Features | Online | Feature Level |
| --------------------------------------------------------------------------------------------------------------------------- | :-----: | :---: | :---------: | :--: | :------------------: | :----: | :-----------: |
| [Kolmogorov-Smirnov](https://github.com/SeldonIO/alibi-detect/blob/master/docs-gb/cd/methods/ksdrift.ipynb)                 |    ✔    |   ✔   |             |   ✔  |           ✔          |        |       ✔       |
| [Cramér-von Mises](https://github.com/SeldonIO/alibi-detect/blob/master/docs-gb/cd/methods/cvmdrift.ipynb)                  |    ✔    |   ✔   |             |      |                      |    ✔   |       ✔       |
| [Fisher's Exact Test](https://github.com/SeldonIO/alibi-detect/blob/master/docs-gb/cd/methods/fetdrift.ipynb)               |    ✔    |       |             |      |           ✔          |    ✔   |       ✔       |
| [Least-Squares Density Difference](https://github.com/SeldonIO/alibi-detect/blob/master/docs-gb/cd/methods/lsdddrift.ipynb) |    ✔    |   ✔   |             |   ✔  |           ✔          |    ✔   |               |
| [Maximum Mean Discrepancy (MMD)](https://github.com/SeldonIO/alibi-detect/blob/master/docs-gb/cd/methods/mmddrift.ipynb)    |    ✔    |   ✔   |             |   ✔  |           ✔          |    ✔   |               |
| [Learned Kernel MMD](https://github.com/SeldonIO/alibi-detect/blob/master/docs-gb/cd/methods/learnedkerneldrift.ipynb)      |    ✔    |   ✔   |      ✔      |   ✔  |                      |        |               |
| [Context-aware MMD](https://github.com/SeldonIO/alibi-detect/blob/master/docs-gb/cd/methods/contextmmddrift.ipynb)          |    ✔    |   ✔   |      ✔      |   ✔  |           ✔          |        |               |
| [Chi-Squared](https://github.com/SeldonIO/alibi-detect/blob/master/docs-gb/cd/methods/chisquaredrift.ipynb)                 |    ✔    |       |             |      |           ✔          |        |       ✔       |
| [Mixed-type tabular](https://github.com/SeldonIO/alibi-detect/blob/master/docs-gb/cd/methods/tabulardrift.ipynb)            |    ✔    |       |             |      |           ✔          |        |       ✔       |
| [Classifier](https://github.com/SeldonIO/alibi-detect/blob/master/docs-gb/cd/methods/classifierdrift.ipynb)                 |    ✔    |   ✔   |      ✔      |   ✔  |           ✔          |        |               |
| [Spot-the-diff](https://github.com/SeldonIO/alibi-detect/blob/master/docs-gb/cd/methods/spotthediffdrift.ipynb)             |    ✔    |   ✔   |      ✔      |   ✔  |           ✔          |        |       ✔       |
| [Classifier Uncertainty](https://github.com/SeldonIO/alibi-detect/blob/master/docs-gb/cd/methods/modeluncdrift.ipynb)       |    ✔    |   ✔   |      ✔      |   ✔  |           ✔          |        |               |
| [Regressor Uncertainty](https://github.com/SeldonIO/alibi-detect/blob/master/docs-gb/cd/methods/modeluncdrift.ipynb)        |    ✔    |   ✔   |      ✔      |   ✔  |           ✔          |        |               |

All drift detectors and built-in preprocessing methods support both **PyTorch** and **TensorFlow** backends. The preprocessing steps include randomly initialized encoders, pretrained text embeddings to detect drift on using the [transformers](https://github.com/huggingface/transformers) library and extraction of hidden layers from machine learning models. The preprocessing steps allow to detect different types of drift such as covariate and predicted distribution shift.