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Hyperparameter Tuning in Azure Machine Learning

Dinesh Asanka takes us through hyperparameter tuning with Azure Machine Learning’s designer:

In the above experiment, both the previous model and the TMH included the model so that we can compare both models. In the above experiment, Tune Model Hyperparameters control is inserted between the Split Data and Score Model controls as shown. In the TMH, control has three inputs. The first control needs the relevant technique and, in this scenario, it is the Two-Class Logistic Regression technique. The second input needs the train data set and the last input needs the evaluation data set and for that, the test data set can be used.

Tune Model Hyperparameters control provides the best combinations and it will be connected to the score model. After the test data stream is connected to the score model, the output of the model is connected to the second input of the Evaluate model so that the previous model and the tuned model can be compared.

I’m not sure if there’s something handled internally in the Tune Model Hyperparameters component, but based on the pipeline images, I’d actually want two separate Split components so that I ended up with something more like 50-20-30 for training, hyperparameter testing, and validation. The first two pipelines appear to be 70-30-0 instead, and so can give you a false sense of confidence in model quality.