From the course: Automated ML.NET Training, Metrics, and Accuracy
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Demo: Metrics - ML.NET Tutorial
From the course: Automated ML.NET Training, Metrics, and Accuracy
Demo: Metrics
- [Instructor] For an example of metrics used in binary classification, we're going to use the sample code where it is referenced in GitHub and it's going to serve as the basis for this demo. Although it's slightly modified, essentially all the key components are there, primarily looking at program .CS within the solution, you'll see that the program .CS is within the trainer project, and more specifically, the whole intent of program .CS is basically to call various methods for instantiating the ML context object, preparing the dataset, loading the dataset, training the model and evaluating the model. We're going to focus on the evaluate model method because that's where we're going to be able to get our metrics. So let's jump to that. And you see that once we're in the evaluate model method, now we have access to our metrics, all thanks to this evaluate method that's available through the binary classification task, which is accessible through the ML context object. So once we call…
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