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azureazure-machine-learning-serviceautomlazure-auto-ml

K-fold cross validation in azure ML

I am currently training a model using an azure ML pipeline that i build with sdk. I am trying to add cross-validation to my ml step. I have noticed that you can add this in the parameters when you configure the autoML. My dataset consists of 30% label 0 and 70% label 1.

My question is, does azure autoML stratify data when performing the cross-validation? If not i would have to do the split/stratify myself before passing it to autoML.

Solution

  • Auto ML can stratify the data when performing cross-validation. The following procedure needs to be followed to perform cross-validation

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    Create the workspace resource.

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    After giving all the details, click on create

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    Launch the Studio and go to AutoML and click on New Automated ML job

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    Upload the dataset from here and give the basic details required.

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    Dataset uploaded with some basic categories

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    After uploading dataset use that dataset for the prediction model performance

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    Here for prediction, we can choose the k-fold cross validation for validation type and number of cross validations as 5. There is no split we are performing. The model will perform according to the validation requirements.