Abstract
Explainable artificial intelligence is an emerging research direction helping the user or developer of machine learning models understand why models behave the way they do. The most popular explanation technique is feature importance. However, there are several different approaches how feature importances are being measured, most notably global and local. In this study we compare different feature importance measures using both linear (logistic regression with L1 penalization) and non-linear (random forest) methods and local interpretable model-agnostic explanations on top of them. These methods are applied to two datasets from the medical domain, the openly available breast cancer data from the UCI Archive and a recently collected running injury data. Our results show that the most important features differ depending on the technique. We argue that a combination of several explanation techniques could provide more reliable and trustworthy results. In particular, local explanations should be used in the most critical cases such as false negatives.
Highlights
Classification models have two main objectives [9]
We focus on feature importance or saliency techniques, that is, techniques that explain the decision of an algorithm by assigning values that reflect the importance of input components in their contribution to that decision [36]
Bolded are the nine features detected with random forest and nine most important features with regression, ranked based on the p-value
Summary
Classification models have two main objectives [9] They should perform well, meaning they should forecast the output for new given input features as accurately as possible. Simple linear classification models are easy to understand and interpret but typically perform worse than non-linear models [10, 19, 24, 44], while complex prediction models with non-linear combinations of features tend to perform better (e.g., [32, 33, 41]) but are less interpretable In other words, they often do a better job in classifying new instances correctly, but the reasons why a certain classification was made is hidden. These models often do not provide enough insight to the classification, which would be needed to employ them in sensitive domains
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