Algorithm overview

This page provides a high-level overview of the algorithms and their features currently implemented in Alibi.

Model Explanations

These algorithms provide instance-specific (sometimes also called local) explanations of ML model predictions. Given a single instance and a model prediction they aim to answer the question “Why did my model make this prediction?” Most of the following algorithms work with black-box models meaning that the only requirement is to have access to a prediction function (which could be an API endpoint for a model in production).

The following table summarizes the capabilities of the current algorithms:

Method	Models	Exp. types	Classification	Regression	Tabular	Text	Image	Cat. data	Train	Dist.
ALE	BB	global	✔	✔	✔				✔
Anchors	BB	local	✔		✔	✔	✔	✔	For Tabular
CEM	BB* TF/Keras	local	✔		✔		✔		Optional
Counterfactuals	BB* TF/Keras	local	✔		✔		✔		No
Prototype Counterfactuals	BB* TF/Keras	local	✔		✔		✔	✔	Optional
Counterfactuals with RL	BB	local	✔		✔		✔	✔	✔
Integrated Gradients	TF/Keras	local	✔	✔	✔	✔	✔	✔	Optional
Kernel SHAP	BB	local global	✔	✔	✔			✔	✔	✔
Tree SHAP	WB	local global	✔	✔	✔			✔	Optional

Key:

• 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

• Cat. data - support for categorical features

• Train - whether a training set is required to fit the explainer

• Dist. - whether a batch of explanations can be executed in parallel

Accumulated Local Effects (ALE): calculates first-order feature effects on the model with respect to a dataset. Intended for use on tabular datasets, currently supports numerical features. Documentation, regression example, classification example.

Anchor Explanations: produce an “anchor” - a small subset of features and their ranges that will almost always result in the same model prediction. Documentation, tabular example, text classification, image classification.

Contrastive Explanation Method (CEM): produce a pertinent positive (PP) and a pertinent negative (PN) instance. The PP instance finds the features that should be minimally and sufficiently present to predict the same class as the original prediction (a PP acts as the “most compact” representation of the instance to keep the same prediction). The PN instance identifies the features that should be minimally and necessarily absent to maintain the original prediction (a PN acts as the closest instance that would result in a different prediction). Documentation, tabular example, image classification.

Counterfactual Explanations: generate counterfactual examples using a simple loss function. Documentation, image classification.

Counterfactual Explanations Guided by Prototypes: generate counterfactuals guided by nearest class prototypes other than the class predicted on the original instance. It can use both an encoder or k-d trees to define the prototypes. This method can speed up the search, especially for black box models, and create interpretable counterfactuals. Documentation, tabular example, tabular example with categorical features, image classification.

Model-agnostic Counterfactual Explanations with RL: transform the optimization procedure into an end-to-end learnable process, allowing to generate batches of counterfactual instances in a single forward pass. The method is model-agnostic (does not assume differentiability) and relies only on feedback from model predictions, allows for generating target-conditional counterfactual instances, flexible feature range constraints for numerical and categorical attributes, including the immutability of protected features (e.g. gender, race) and can be easily extended to other data modalities such as images. Documentation, tabular_example, image_classification.

Integrated gradients: attribute an importance score to each element of the input or an internal layer of the the model
with respect to a given baseline. The attributions are calculated as the path integral of the model gradients along a straight line from the baseline to the input. Documentation, MNIST example, Imagenet example, IMDB example. Transformers example.

Kernel Shapley Additive Explanations (Kernel SHAP): attribute the change of a model output with respect to a given baseline (e.g., average over a reference set) to each of the input features. This is achieved for each feature in turn, by averaging the difference in the model output observed when the feature whose contribution is to be estimated is part of a group of “present” input features and the value observed when the feature is excluded from said group. The features that are not “present” (i.e., are missing) are replaced with values from a background dataset. This algorithm can be used to explain regression models and it is optimised to distribute batches of explanations.Documentation, continuous data, more continuous data, categorical data, distributed_batch_explanations

Tree Shapley Additive Explanations (Tree SHAP): attribute the change of a model output with respect to a baseline (e.g., average over a reference set or inferred from node data) to each of the input features. Similar to Kernel SHAP, the shap value of each feature is computed by averaging the difference of the model output observed when the feature is part of a group of “present” features and when the feature is excluded from said group, over all possible subsets of “present” features. Different estimation procedures for the effect of selecting different subsets of “present” features on the model output give rise to the interventional feature perturbation and the path-dependent feature perturbation variants of Tree SHAP. This algorithm can be used to explain regression models. Documentation, interventional feature perturbation Tree SHAP, path-dependent feature perturbation Tree SHAP.

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

Trust scores: produce a “trust score” of a classifier’s prediction. The trust score is the ratio between the distance to the nearest class different from the predicted class and the distance to the predicted class, higher scores correspond to more trustworthy predictions. Documentation, tabular example, image classification

Linearity measure: produces a score quantifying how linear the model is around a test instance. The linearity score measures the model linearity around a test instance by feeding the model linear superpositions of inputs and comparing the outputs with the linear combination of outputs from predictions on single inputs. Documentation Tabular example, image classification

1

depending on model

2

may require dimensionality reduction