For engineers, one approach to explain the behavior of a system is by using first principles. A first-principles model has clear, explainable physical meaning, and its behavior can be parameterized. That type of model is known as “white box.” The behavior of machine learning models is more...
Interpretability has been always present in Machine Learning and Artificial Intelligence. However, it is difficult to measure it (even to define it), and quite commonly it collides with other properties as accuracy, with a clear meaning and well defined metrics. This situation has reduced its ...
v.in·ter·pret·ed,in·ter·pret·ing,in·ter·prets v.tr. 1.To explain the meaning of:The newspapers interpreted the ambassador's speech as an attempt at making peace.See Synonyms atexplain. 2.To understand the significance of; construe:interpreted his smile to be an agreement; interpret...
Interpretabilityis an important consideration for computational models– an ideal model should be bothexplainable, meaning its internal workings and decision-making process should be straightforward to describe in human terms; andinterpretable, so that its actions are understandable to users. This is a ...
and Xnm is Anm then Y is Bn with αn The αi values associated with rule Ri acquires the meaning of how significant or important that rule is for the inference process. The result of the use of rule weights in system modelling is a significant improvement of accuracy15. The rule weight...
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Some of the reasons cited by Molnar [12] in support of interpretability are directly relevant to SCRM: (1) finding meaning withing and gaining the knowledge captured by machine learning models; (2) detecting bias in models; and (3) increase acceptance of produced solutions. In the context of...
In model fairness terms, this is measured asequalized odds, meaning that you had just as likely a chance of your application being rejected as other people who had their application rejected. And conversely, you had just as good a chance of being accepted to university as others who were acc...
we see that the four data-generating predictors (education, color, density, and crime) have relatively large values, meaning that they have predictive power in our model. On the other hand, the five dummy predictors have relatively small values, meaning that they are not as useful for making...
[11] Ribeiro, Singh and Guestrin.“Why should I trust you?” Explaining the predictions of any classifier, ACM 2016 [12] Bender, Koller. “Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data”, ACL 2020NLP