Abstract
Numerous sophisticated machine learning tools (e.g. ensembles or deep networks) have shown outstanding performance in terms of accuracy on different numeric forecasting tasks. In many real world application domains the numeric predictions of the models drive important and costly decisions. Frequently, decision makers require more than a black box model to be able to “trust” the predictions up to the point that they base their decisions on them. In this context, understanding these black boxes has become one of the hot topics in Machine Learning and Data Mining research. This paper proposes a series of visualisation tools that help in understanding the predictive performance of non-interpretable regression models. More specifically, these tools allow the user to relate the expected error of any model to the values of the predictor variables. This type of information allows end-users to correctly assess the risks associated with the use of the models, by showing how concrete values of the predictors may affect the performance of the models. Our illustrations with different real world data sets and learning algorithms provide insights on the type of usage and information these tools bring to both the data analyst and the end-user.
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Notes
- 1.
Please note that below each bin we provide the number of training cases belonging to each bin and the respective percentage of the full data set.
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Acknowledgments
This work is partially funded by Portuguese Science and Technology Foundation (FCT) through the NITROLIMIT project (PTDC/CTA-AMB/30997/2017) and the project UID/EEA/50014/2019. The work of L. Torgo was undertaken, in part, thanks to funding from the Canada Research Chairs program.
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Areosa, I., Torgo, L. (2019). Visual Interpretation of Regression Error. In: Moura Oliveira, P., Novais, P., Reis, L. (eds) Progress in Artificial Intelligence. EPIA 2019. Lecture Notes in Computer Science(), vol 11805. Springer, Cham. https://doi.org/10.1007/978-3-030-30244-3_39
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