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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/13147 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/13147 http://eprints.org/relation/isCoversheetVersionOf https://pure.iiasa.ac.at/id/document/13147 archive 351 disk0/00/00/84/23 2016-01-15 08:40:28 2021-08-27 17:20:10 2016-01-15 08:40:28 monograph show 2 Kotlowski W. 8181 pub iis_cli iis_fag iis_mod prog_ime prog_ysp This report presents application of machine learning methodology in modeling process. The objective is to identify and explain impact of weather variations on crop yields, and to test the approach on the agricultural and climatic data from the USA. First, separation of weather and non-weather factors is performed by trend identification- two methods of trend identification are considered. Then the importance of the attributes is assessed using information gain measure and attributes are also aggregated into seasons. Finally, four types of classification methods (support vector machines, nearestneighbors classifier and two variants of decision rules induction) are applied to the data and the results are compared and analyzed. The proposed approach differs from standard approaches to the crop yields modeling. It does not require a lot of expert knowledge, nor assume anything about the data distributions. All conclusions are drawn from data and the final model is build only from data. This approach is much simpler, however it maintains high accuracy and performance 2007-11 published IR-07-034 IR-07-034 1385 public interim_report IIASA, Laxenburg, Austria 38 FALSE info:eu-repo/semantics/book