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Alibi is an open source Python library aimed at machine learning model inspection and interpretation.
The focus of the library is to provide high-quality implementations of black-box, white-box, local and global
explanation methods for classification and regression models.

If you’re interested in outlier detection, concept drift or adversarial instance detection, check out our sister project alibi-detect.

Table of Contents

Installation and Usage

Alibi can be installed from:

  • PyPI or GitHub source (with pip)
  • Anaconda (with conda/mamba)

With pip

  • Alibi can be installed from PyPI:

  • Alternatively, the development version can be installed:

    pip install git+ 
  • To take advantage of distributed computation of explanations, install alibi with ray:

  • For SHAP support, install alibi as follows:

With conda

To install from conda-forge it is recommended to use mamba,
which can be installed to the base conda enviroment with:

conda install mamba -n base -c conda-forge
  • For the standard Alibi install:

    mamba install -c conda-forge alibi
  • For distributed computing support:

    mamba install -c conda-forge alibi ray
  • For SHAP support:

    mamba install -c conda-forge alibi shap


The alibi explanation API takes inspiration from scikit-learn, consisting of distinct initialize,
fit and explain steps. We will use the AnchorTabular
explainer to illustrate the API:

from alibi.explainers import AnchorTabular

# initialize and fit explainer by passing a prediction function and any other required arguments
explainer = AnchorTabular(predict_fn, feature_names=feature_names, category_map=category_map)

# explain an instance
explanation = explainer.explain(x)

The explanation returned is an Explanation object with attributes meta and data. meta is a dictionary
containing the explainer metadata and any hyperparameters and data is a dictionary containing everything
related to the computed explanation. For example, for the Anchor algorithm the explanation can be accessed
via['anchor'] (or explanation.anchor). The exact details of available fields varies
from method to method so we encourage the reader to become familiar with the
types of methods supported.

Supported Methods

The following tables summarize the possible use cases for each method.

Model Explanations

Model Confidence

These algorithms provide instance-specific scores measuring the model confidence for making a
particular prediction.

Method Models Classification Regression Tabular Text Images Categorical Features Train set required
Trust Scores BB (1) (2) Yes
Linearity Measure BB Optional


  • BB – black-box (only require a prediction function)
  • BB* – black-box but assume model is differentiable
  • WB – requires white-box model access. There may be limitations on models supported
  • TF/Keras – TensorFlow models via the Keras API
  • Local – instance specific explanation, why was this prediction made?
  • Global – explains the model with respect to a set of instances
  • (1) – depending on model
  • (2) – may require dimensionality reduction


These algorithms provide a distilled view of the dataset and help construct a 1-KNN interpretable classifier.

Method Classification Regression Tabular Text Images Categorical Features Train set labels
ProtoSelect Optional

References and Examples


If you use alibi in your research, please consider citing it.

BibTeX entry:

  author  = {Janis Klaise and Arnaud Van Looveren and Giovanni Vacanti and Alexandru Coca},
  title   = {Alibi Explain: Algorithms for Explaining Machine Learning Models},
  journal = {Journal of Machine Learning Research},
  year    = {2021},
  volume  = {22},
  number  = {181},
  pages   = {1-7},
  url     = {}

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