Interpretability: Difference between revisions

Interpretability (view source)

280 bytes removed , 25 February 2023

no edit summary

7,785

edits

@@ Line 1: / Line 1: @@
 {{see also|Machine learning terms}}
-==Interpretability in Machine Learning==
+==Introduction==
-Interpretability in machine learning refers to the process of comprehending and explaining the actions taken by a model. It plays an essential role in developing these models, particularly in fields such as healthcare, finance and criminal justice where decisions made by these algorithms may have far-reaching repercussions for individuals and society at large.
+[[Interpretability]] in [[machine learning]] refers to the process of comprehending and explaining the actions taken by a [[model]]. It's goal is to explain a [[machine learning model]]'s reasoning and make them understandable to humans. This is accomplished by providing insights into how the model makes [[prediction]]s, what [[features]] it takes into account and how different elements interact with one another.
-Interpretability is the goal of interpretability, which seeks to make machine learning models more transparent, reliable and accountable. This is accomplished by providing insights into how the model makes predictions, what features it takes into account and how different elements interact with one another.
 ==Types of Interpretability==