Lighting the black box: explaining individual predictions of machine learning algorithms
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...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/113463 |
| Acceso en línea: | https://hdl.handle.net/2117/113463 |
| Access Level: | acceso abierto |
| Palabra clave: | Artificial intelligence Machine learning Interpretability Intel·ligència artificial Classificació AMS::68 Computer science::68T Artificial intelligence Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
| Sumario: | 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. |
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