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Exam AWS Certified Machine Learning - Specialty All Questions

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Exam AWS Certified Machine Learning - Specialty topic 1 question 318 discussion

An insurance company is creating an application to automate car insurance claims. A machine learning (ML) specialist used an Amazon SageMaker Object Detection - TensorFlow built-in algorithm to train a model to detect scratches and dents in images of cars. After the model was trained, the ML specialist noticed that the model performed better on the training dataset than on the testing dataset.

Which approach should the ML specialist use to improve the performance of the model on the testing data?

  • A. Increase the value of the momentum hyperparameter.
  • B. Reduce the value of the dropout_rate hyperparameter.
  • C. Reduce the value of the learning_rate hyperparameter
  • D. Increase the value of the L2 hyperparameter.
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Suggested Answer: D 🗳️

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Peter_Hsieh
5 months, 4 weeks ago
Selected Answer: D
D If your model is overfitting the training data, it makes sense to take actions that reduce model flexibility. To reduce model flexibility, try the following: Feature selection: consider using fewer feature combinations, decrease n-grams size, and decrease the number of numeric attribute bins. Increase the amount of regularization used. https://docs.aws.amazon.com/machine-learning/latest/dg/model-fit-underfitting-vs-overfitting.html
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vkbajoria
6 months, 4 weeks ago
Selected Answer: D
Increase the value of L2 regularization
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AIWave
7 months, 2 weeks ago
Selected Answer: D
A- No - momentum is for SGD with momentum, N/A in this case B: No - reducing dropout may or may not help C: No - reducing learning rate may increase overfitting even further D: Yes - L2 regularization penalizes large weights in the model. Increasing can help prevent overfitting by encouraging smaller weights.
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