Abstract
This chapter describes how we used regression rules to improve upon results previously published in the Earth science literature. In such a scientific application of machine learning, it is crucially important for the learned models to be understandable and communicable. We recount how we selected a learning algorithm to maximize communicability, and then describe two visualization techniques that we developed to aid in understanding the model by exploiting the spatial nature of the data. We also report how evaluating the learned models across time let us discover an error in the data.
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Schwabacher, M., Langley, P., Potter, C., Klooster, S., Torregrosa, A. (2007). Discovering Communicable Models from Earth Science Data. In: Džeroski, S., Todorovski, L. (eds) Computational Discovery of Scientific Knowledge. Lecture Notes in Computer Science(), vol 4660. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73920-3_7
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DOI: https://doi.org/10.1007/978-3-540-73920-3_7
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