Unsupervised machine learning with multivariate Gaussian mixture model.
Open source
24 alternativesAlibi
Alibi explains machine learning model predictions through a set of algorithms.
About Alibi
Alibi is a source-available Python library for inspection and interpretation of classification and regression models. It provides black-box, white-box, local and global explanation methods for tabular, text and image inputs. It includes methods such as Anchors, counterfactual variants, Integrated Gradients, Accumulated Local Effects, SHAP variants, and confidence scores. Some methods require training data, white-box access or specific frameworks, and SHAP and distributed computing need optional installs.
Key features
- Local and global explanations for classification and regression models
- Anchor explanations for tabular, text and image inputs
- Counterfactual examples including prototype and RL variants
- Integrated Gradients and SHAP variants with optional SHAP install
- Trust Scores and Linearity Measure for instance confidence
- Optional distributed explanation computation with Ray
Best for: Python users who need to inspect classification or regression model predictions with local or global explanations.
Good for
- Covers tabular, text and image inputs across methods
- Installable from PyPI and conda-forge
- Includes both prediction explanations and model-confidence scores
Watch out for
- Some methods require training data, white-box access or TensorFlow/Keras
- SHAP and Ray functionality require separate optional installs
- Outlier detection, concept drift and adversarial detection are directed to sister project alibi-detect
Alibi at a glance
- Website
- github.com/SeldonIO/alibi
- Category
- AI & Machine Learning › Models & ML Research
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Alibi FAQ
How is Alibi installed?
From PyPI with pip install alibi, from GitHub source, or from conda-forge with mamba install -c conda-forge alibi, with optional extras alibi[ray] and alibi[shap].
How does the explanation API work?
Initialize an explainer with a prediction function and required arguments, fit it where needed, then call explain on an instance, which returns an Explanation object with meta and data attributes.
Does Alibi handle outlier detection or concept drift?
No. For outlier detection, concept drift or adversarial instance detection, the documentation points to the sister project alibi-detect.
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