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Question 306 - MLS-C01 discussion

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A machine learning (ML) specialist is building a credit score model for a financial institution. The ML specialist has collected data for the previous 3 years of transactions and third-party metadata that is related to the transactions.

After the ML specialist builds the initial model, the ML specialist discovers that the model has low accuracy for both the training data and the test data. The ML specialist needs to improve the accuracy of the model.

Which solutions will meet this requirement? (Select TWO.)

A.

Increase the number of passes on the existing training data. Perform more hyperparameter tuning.

Answers
A.

Increase the number of passes on the existing training data. Perform more hyperparameter tuning.

B.

Increase the amount of regularization. Use fewer feature combinations.

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B.

Increase the amount of regularization. Use fewer feature combinations.

C.

Add new domain-specific features. Use more complex models.

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C.

Add new domain-specific features. Use more complex models.

D.

Use fewer feature combinations. Decrease the number of numeric attribute bins.

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D.

Use fewer feature combinations. Decrease the number of numeric attribute bins.

E.

Decrease the amount of training data examples. Reduce the number of passes on the existing training data.

Answers
E.

Decrease the amount of training data examples. Reduce the number of passes on the existing training data.

Suggested answer: A, C

Explanation:

For a model with low accuracy on both training and testing datasets, the following two strategies are effective:

Increase the number of passes and perform hyperparameter tuning: This approach allows the model to better learn from the existing data and improve performance through optimized hyperparameters.

Add domain-specific features and use more complex models: Adding relevant features that capture additional information from domain knowledge and using more complex model architectures can help the model capture patterns better, potentially improving accuracy.

Options B, D, and E would either reduce feature complexity or training data volume, which is less likely to improve performance when accuracy is low on both training and testing sets.

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asked 31/10/2024
Marcel Engelbrecht
44 questions
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