Dr. James McCaffrey of Microsoft Research says decision trees are useful for relatively small datasets and when the trained model must be easily interpretable, but often don't work well with large ...

Decision trees are useful for relatively small datasets that have a relatively simple underlying structure, and when the trained model must be easily interpretable, explains Dr. James McCaffrey of ...

The visual data mining process, seen in the first part of this two-part article, revealed patterns in four dimensions between cumulative gas well production and independent variables. "Jump ...

Dermatologists typically classify skin lesions based on multiple data sources. Algorithms that fuse the information together can support this classification. An international research team has now ...

This is a preview. Log in through your library . Abstract Objective: Classification tree analysis is a potentially powerful tool for investigating multilevel interactions. Within the context of colon ...

The cotton bollworm, Helicoverpa armigera (Hϋbner) is one of the most important pests affecting crop production globally. The data-mining technique, for predicting pest incidence using biotic and ...

Machine learning and deep learning have been widely embraced, and even more widely misunderstood. In this article, I’ll step back and explain both machine learning and deep learning in basic terms, ...

Here is a [recently developed] tool for analyzing the choices, risks, objectives, monetary gains, and information needs involved in complex management decisions, like plant investment. by John F.