Automate machine learning model selection with Azure Machine Learning

Question 1

What is Automated Machine Learning

Answer 1

Automated Machine Learning enables you to try multiple Algorithms and preprocessing transformations with you data. This, combined with scalable cloud-based compute makes it possible to find the best performing model for your data without the huge amount of time-consuming manual trial and error that would otherwise be required

Question 2

Where can you run Azure Machine Learning SDK to run automated machine learning experiments? Basic or Enterprise edition or both

Answer 2

Both - god how generous Microsoft is…

Question 3

For which model types can you use automated machine learning in AML to train?

Answer 3

Classification
Regression
TIme Series Forecasting

Question 4

What is it with pre-processing and featurization within automated machine learning. How do you apply for example scaling and normalization

Answer 4

Automated machine learning applies scaling and normalization to numeric data automatically, helping prevent any large-scale features from dominating training. During an automated machine learning experiment, multiple scaling or normalization techniques will be applied

Question 5

What options do you have to run an automated machine learning experiment

Answer 5

You can either use the user interface in Azure Machine Learning studio, or submit an experiment using the SDK.

Question 6

What options are available for specifying data for training within Automated Machine Learning

Answer 6

When using the SDK to run an automated machine learning experiment, you can submit the data in the following ways:

• Specify a dataset or dataframe of training data that includes features and the label to be predicted
• Optionally, specify a second validation data dataset or dataframe that will be used to validate the trained model. If this is not provided, AML will apply cross-validation using the training data

Alternatively: use numpy arrays

Question 7

You have submitted an automated machine learning run using the Python SDk for Azure Machine Learning. When the run completes, which method of the run object should you use to retrieve the best model?

get_output()

load_model()

get_metrics()

Answer 7

get_output()
That is correct. The get_output method of an automated machine learning run returns the best mode and the child run that trained it.