For your machine learning experiments, you are going to use the Scikit-Learn framework.
You want to keep your Python code defining the run configuration as simple and compact as possible.
Which is the best way to achieve this goal?
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: B.
Option A is incorrect because while this solution can be used to set the run configuration, in the case of Scikit-Learn framework, using the pre-configured SKLearn estimator is the best solution.
Option B is CORRECT because the simplest way to define the run configuration for the learning script built on a given ML framework (like Scikit-Learn) is to use the framework-specific estimators.
Option C is incorrect because while this can be used to set the run configuration, in the case of Scikit-Learn framework, using the pre-configured SKLearn estimator is the best solution.
Option D is incorrect because the specific ML packages (like ScikitLearn, PyTorch etc.) are not contained in the base configuration.
If you need Scikit-Learn, you have to add it to your run configuration (either via ScriptRunConfig or via an estimator).
Reference:
To keep the Python code defining the run configuration simple and compact while using the Scikit-Learn framework for machine learning experiments, the best approach is to use the pre-configured estimator provided by the Azure Machine Learning service.
Option A suggests using the CondaDependencies.create() method to define the environment and use it as the environment_definition parameter of an Estimator. This approach may work, but it can be time-consuming and requires specifying all the dependencies manually, which can be error-prone.
Option B suggests importing the SKLearn package and using the SKLearn pre-configured estimator to define the run configuration. This is a more straightforward approach that does not require defining the environment or specifying the dependencies manually. The SKLearn estimator is pre-configured with Scikit-Learn and other commonly used machine learning libraries such as NumPy and Pandas. This approach is the recommended approach for using Scikit-Learn in Azure Machine Learning service.
Option C suggests importing the Estimator package and using the conda_packages parameter to specify the dependencies. This approach is similar to Option A, but using the Estimator package simplifies the configuration process.
Option D is incorrect because the Azure Machine Learning environments are not pre-configured for Scikit-Learn. Even though the environment includes many commonly used packages, Scikit-Learn may not be included, and it is best to use the pre-configured estimator provided by the Azure Machine Learning service.
In summary, the best approach to keep the Python code defining the run configuration simple and compact while using the Scikit-Learn framework for machine learning experiments is to import the SKLearn package and use the pre-configured estimator provided by the Azure Machine Learning service.