Which ONE of the following options is the MOST APPROPRIATE stage of the ML workflow to set model and algorithm hyperparameters?

Question

Options

• AEvaluating the model
• BDeploying the model
• CTuning the model
• DData testing

• A
  3% (2)
• B
  5% (3)
• C
  83% (54)
• D
  9% (6)

Explanation

Setting model and algorithm hyperparameters is an essential step in the machine learning workflow, primarily occurring during the tuning phase. Evaluating the model (A): This stage involves assessing the model's performance using metrics and does not typically include the setting of hyperparameters. Deploying the model (B): Deployment is the stage where the model is put into production and used in real-world applications. Hyperparameters should already be set before this stage. Tuning the model (C): This is the correct stage where hyperparameters are set. Tuning involves adjusting the hyperparameters to optimize the model's performance. Data testing (D): Data testing involves ensuring the quality and integrity of the data used for training and testing the model. It does not include setting hyperparameters. Hence, the most appropriate stage of the ML workflow to set model and algorithm hyperparameters is C. Tuning the model.

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