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Techniques for dealing with hyperparameter optimization

Techniques for dealing with hyperparameter optimization - Azure Tutorial

From the course: Microsoft Azure Data Scientist Associate (DP-100) Cert Prep

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Techniques for dealing with hyperparameter optimization

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- [Instructor] Here is a SDK version 1 example notebook that I'm going to share a few ideas about in terms of hyper parameter settings. And notice here that it calls that out at the very beginning. And at the start there's some basic imports here, like importing pandas, and the Azure SDK v1. And once the data is pulled into pandas, it's cleaned, finally the workspace is configured, the data is then put into a test and train split. So nothing, you know, different than a traditional machine learning model yet. But here's where things get interesting is that if we select the Automatically Train Model option right here, one of the things that we can do here is actually take a look at the training settings that are important to pay attention to. So when you're setting up automatic hyperparameter tuning, there are a few things to be aware of. So one of them is iteration timeout in minutes. And so this would be the time that is spent in each iteration. So you have to decide how long you'd…

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