MCQ IN COMPUTER SCIENCE & ENGINEERING

COMPUTER SCIENCE AND ENGINEERING

MACHINE LEARNING

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Question [CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]

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?

A	Grid Search
B	Random Search
C	Bayesian optimization
D	None of the above

Explanation:

Detailed explanation-1: -One traditional and popular way to perform hyperparameter tuning is by using an Exhaustive Grid Search from Scikit learn. This method tries every possible combination of each set of hyper-parameters. Using this method, we can find the best set of values in the parameter search space.

Detailed explanation-2: -Amazon SageMaker automatic model tuning (AMT), also known as hyperparameter tuning, finds the best version of a model by running many training jobs on your dataset. To do this, AMT uses the algorithm and ranges of hyperparameters that you specify.

Detailed explanation-3: -What Amazon SageMaker option should the company use to train their ML models that reduces the management and automates the pipeline for future retraining? Create and train your XGBoost algorithm on your local laptop and then use an Amazon SageMaker endpoint to host the ML model.

There is 1 question to complete.