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You are designing an Azure Machine Learning workflow.
You have a dataset that contains two million large digital photographs.
You plan to detect the presence of trees in the photographs.
You need to ensure that your model supports the following:
Hidden layers that support a directed graph structure
User-defined core components on the GPU
Solution: You create a Machine Learning experiment that implements the Multiclass Decision Jungle module.
Does this meet the goal?
A.
Yes
B.
No
Decision jungles are a recent extension to decision forests. A decision jungle consists of an ensemble of decision directed acyclic graphs (DAGs).
Decision jungles have the following advantages:
• By allowing tree branches to merge, a decision DAG typically has a lower memory footprint and a better generalization performance than a decision tree, albeit at the cost of a somewhat higher training time.
• Decision jungles are non-parametric models, which can represent non-linear decision boundaries.
• They perform integrated feature selection and classification and are resilient in the presence of noisy features.
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