Abstract: Abstract Soil drainage conditions are highly important to farmers and the environment. To map drainage classes efficiently, several analytical approaches, such as decision tree classification, can be used. Decision tree classification can be improved by combining the predictions of several trees with boosting and bagging techniques. This study tested the relative performance of boosting and bagging for the prediction of drainage classes. Furthermore, as drainage classes form an ordered series rather than unrelated classes, differential costs fo...
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Topics: 
Machine learning
Statistics
Data mining