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Lighting the black box: explaining individual predictions of machine learning algorithms

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hdl:2117/113463

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Ferrando Hernández, Pol
Tutor / directorDelicado Useros, Pedro FranciscoMés informacióMés informacióMés informació; Belanche Muñoz, Luis AntonioMés informacióMés informacióMés informació
Document typeMaster thesis
Date2018-01
Rights accessOpen Access
Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain
Abstract
Many machine learning techniques remain ''black boxes'' because, despite their high predictive performance, it is difficult to understand the role of each variable involved in the prediction task. In this thesis, we will study three methods that explain individual predictions of any model and determine what explanatory variables are most influential for each particular observation, which brings transparency to machine learning algorithms. Additionally, we will test these methods on a simple dataset for which we can assess the quality of the explanations.
SubjectsArtificial intelligence, Intel·ligència artificial
DegreeMÀSTER UNIVERSITARI EN ESTADÍSTICA I INVESTIGACIÓ OPERATIVA (Pla 2013)
URIhttp://hdl.handle.net/2117/113463
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