Accumulated Local Effects
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Accumulated local effects (ALE) is a
machine learning Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of Computational statistics, statistical algorithms that can learn from data and generalise to unseen data, and thus perform Task ( ...
interpretability method.


Concepts

ALE uses a conditional feature distribution as an input and generates augmented data, creating more realistic data than a marginal distribution. It ignores far out-of-distribution (outlier) values. Unlike partial dependence plots and marginal plots, ALE is not defeated in the presence of correlated predictors. It analyzes differences in predictions instead of averaging them by calculating the average of the differences in model predictions over the augmented data, instead of the average of the predictions themselves.


Example

Given a model that predicts house prices based on its distance from city center and size of the building area, ALE compares the differences of predictions of houses of different sizes. The result separates the impact of the size from otherwise correlated features.


Limitations

Defining evaluation windows is subjective. High correlations between features can defeat the technique. ALE requires more and more uniformly distributed observations than PDP so that the
conditional distribution Conditional (if then) may refer to: * Causal conditional, if X then Y, where X is a cause of Y *Conditional probability, the probability of an event A given that another event B * Conditional proof, in logic: a proof that asserts a conditional, ...
can be reliably determined. The technique may produce inadequate results if the data is highly sparse, which is more common with high-dimensional data (
curse of dimensionality The curse of dimensionality refers to various phenomena that arise when analyzing and organizing data in high-dimensional spaces that do not occur in low-dimensional settings such as the three-dimensional physical space of everyday experience. T ...
).


See also

* Interpretability (machine learning)


References


External links

* {{Cite book , first=Michael , last=Munn , url=http://worldcat.org/oclc/1350433516 , title=Explainable AI for Practitioners , date=2022 , publisher=O'Reilly Media, Incorporated , isbn=978-1-0981-1910-2 , oclc=1350433516 Machine learning algorithms