Microsoft Certified Azure AI Fundamentals · Free Practice Question Medium
Question 24
Which technique involves halting the training process as soon as the model’s performance on the validation set begins to decline, thereby helping prevent overfitting?
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A
Dimension reduction
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B
Regularization
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C
Cross validation
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D
Early stopping
Reveal correct answer
Correct answer: D
Explanation
Early Stopping Technique
Early stopping is a regularization method used during the training of machine learning models to prevent overfitting. This technique involves monitoring the model's performance on a validation set during training. As training progresses, the model typically improves its performance on the training set, but at some point, the performance on the validation set starts to decline, indicating that the model is beginning to overfit the training data. Early stopping halts the training process at this point, thereby preserving a model state that generalizes better to unseen data.
Why Early Stopping is Effective
Prevents Overfitting: By stopping training at the optimal point, the model avoids learning noise and specific patterns from the training set that do not apply to new data.
Saves Computational Resources: Training can be stopped before completing all planned epochs, which reduces the computational cost.
Maintains Generalization: The model's performance on the validation set is used as a proxy for its performance on unseen data, ensuring that the final model has balanced complexity and robustness.
Comparison with Other Techniques
Cross Validation:
While cross validation is a powerful method for estimating model performance and selecting hyperparameters, it does not directly halt the training process to prevent overfitting.Regularization:
Regularization adds a penalty to the loss function to discourage overly complex models. However, it does not inherently stop training when validation performance declines.Dimension Reduction:
Dimension reduction techniques reduce the number of features in the data to simplify the model, but they are not used to monitor or control the training process based on validation performance.
Microsoft's Educational Content For This Topic:
A.
Dimension reduction (e.g., PCA) simplifies the feature space but does not directly address the point at which training should be halted.
B.
This technique adds a penalty to the loss function to discourage overly complex models, but it does not involve stopping the training process.
C.
This technique divides data into multiple training and validation sets to assess generalization but does not directly stop training based on performance degradation.
D.
Early stopping monitors validation performance during training and stops the process when performance begins to worsen, thereby preventing the model from overfitting to the training data.
Discussion
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