AI-900 · Question #171
During the process of Machine Learning, when should you review evaluation metrics.
The correct answer is D. After you test a model on the validation data. Evaluation metrics are critically reviewed after a machine learning model has been trained and tested on validation data to assess its performance and generalization capabilities.
Question
Options
- ABefore you train a model.
- BAfter you clean the data.
- CBefore you choose the type of model.
- DAfter you test a model on the validation data.
How the community answered
(24 responses)- A4% (1)
- C8% (2)
- D88% (21)
Why each option
Evaluation metrics are critically reviewed after a machine learning model has been trained and tested on validation data to assess its performance and generalization capabilities.
Reviewing evaluation metrics before training a model is premature as there is no model performance to evaluate yet.
Reviewing evaluation metrics after cleaning data but before model training or testing is premature, as the model's actual performance has not been established.
Reviewing evaluation metrics before choosing a model type is premature because there is no trained model whose performance can be evaluated at this stage.
Evaluation metrics are used to quantify a model's performance, generalization ability, and suitability for its task, which can only be accurately assessed after the model has completed training and made predictions on unseen validation or test data.
Concept tested: ML Model Evaluation Lifecycle
Source: https://learn.microsoft.com/en-us/azure/machine-learning/concept-what-is-machine-learning?view=azureml-api-2#how-machine-learning-works
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