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Image Classification in Azure Machine Learning

I'm preparing for the Azure Machine Learning exam, and here is a question confuses me.

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:

Solution: You create a Machine Learning experiment that implements the Multiclass Decision Jungle module. Does this meet the goal?

Solution: You create a Machine Learning experiment that implements the Multiclass Neural Network module. Does this meet the goal?

The answer for the first question is No while for second is Yes, but I cannot understand why Multiclass Decision Jungle doesn't meet the goal since it is a classifier. Can someone explain to me the reason?

Solution

  • I suppose that this is part of a series of questions that present the same scenario. And there should be definitely some constraints in the scenario. Moreover if you have a look on the Azure documentation:

    However, recent research has shown that deep neural networks (DNN) with many layers can be very effective in complex tasks such as image or speech recognition. The successive layers are used to model increasing levels of semantic depth.

    Thus, Azure recommends using Neural Networks for image classification. Remember, that the goal of the exam is to test your capacity to design data science solution using Azure so better to use their official documentation as a reference.

    And comparing to the other solutions:

    1. You create an Azure notebook that supports the Microsoft Cognitive Toolkit.
    2. You create a Machine Learning experiment that implements the Multiclass Decision Jungle module.
    3. You create an endpoint to the Computer vision API.
    4. You create a Machine Learning experiment that implements the Multiclass Neural Network module.
    5. You create an Azure notebook that supports the Microsoft Cognitive Toolkit.

    There are only 2 Azure ML Studio modules, and as the question is about constructing a workflow I guess we can only choose between them. (CNTK is actually the best solution as it allows constructing a deep neural network with ReLU whereas AML Studio doesn't, and API call is not about data science at all).

    Finally, I do agree with the other contributors that the question is absurd. Hope this helps.