APPLICATION OF SUPERVISED LEARNING
MACHINE LEARNING PIPELINE
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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A Data Scientist wants to tune the hyperparameters of a machine learning model to improve the model’s F1 score.Which technique CANNOT be used in Amazon SageMaker to solve this problem?
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Grid Search
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Random Search
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Bayesian optimization
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None of the above
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Explanation:
Detailed explanation-1: -Built-in integration with SageMaker Jumpstart and SageMaker Autopilot. SageMaker Automatic Model Tuning is integrated into SageMaker JumpStart, providing one-click fine tuning and deployment of a wide variety of pretrained models across ML tasks, algorithms, and solutions for common business problems.
Detailed explanation-2: -A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm and ranges of hyperparameters that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by a metric that you choose.
There is 1 question to complete.