Random Forest Similarity Maps: A Scalable Visual Representation for Global and Local Interpretation
Machine Learning prediction algorithms have made significant contributions in today’s world, leading to increased usage in various domains. However, as ML algorithms surge, the need for transparent and interpretable models becomes essential. Visual representations have shown to be instrumental in ad...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
MDPI AG
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/7a08344bc0154928a55d6d7975855372 |
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| Sumario: | Machine Learning prediction algorithms have made significant contributions in today’s world, leading to increased usage in various domains. However, as ML algorithms surge, the need for transparent and interpretable models becomes essential. Visual representations have shown to be instrumental in addressing such an issue, allowing users to grasp models’ inner workings. Despite their popularity, visualization techniques still present visual scalability limitations, mainly when applied to analyze popular and complex models, such as Random Forests (RF). In this work, we propose <i>Random Forest Similarity Map (RFMap)</i>, a scalable interactive visual analytics tool designed to analyze RF ensemble models. <i>RFMap</i> focuses on explaining the inner working mechanism of models through different views describing individual data instance predictions, providing an overview of the entire forest of trees, and highlighting instance input feature values. The interactive nature of <i>RFMap</i> allows users to visually interpret model errors and decisions, establishing the necessary confidence and user trust in RF models and improving performance. |
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