In this post, we set up and run our first HPO job using Amazon SageMaker Automatic Model Tuning (AMT). Searching the hyperparameter space for the optimal values is referred to as hyperparameter tuning or hyperparameter optimization (HPO), and should result in a model that gives accurate predictions. Just like turning a knob on a radio receiver to find the right frequency, each hyperparameter should be carefully tuned to optimize performance. And we pay much attention to them because they have a major impact on the ultimate performance of your model. ![]() At any rate, they are of very practical use, such as the number of epochs to train, the learning rate, the max depth of a decision tree, and so forth. Therefore, the parameters that are used to configure the ML training process are then called hyperparameters-parameters describing the creation of parameters. What does that name mean? The result of ML training, the model, can be largely seen as a collection of parameters that are learned during training. ![]() These deferred design decisions manifest themselves as hyperparameters. Typically, algorithms defer some design decisions to the ML practitioner to adopt for their specific data and task. Their performance relies on using the right training data and choosing the right model and algorithm. Machine learning (ML) models are taking the world by storm.
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