How to Resolve Overfitting in Machine Learning Models?
Short answer
B Increase the regularization parameter to decrease model complexity.
Question
Key Takeaways
This question tests your ability to diagnose overfitting and apply regularization to constrain model complexity for better generalization on unseen data.
When an ML model performs well on training data but poorly on new data, it is overfitting. Increasing the regularization parameter reduces model complexity and improves generalization.
Common mistake: Candidates often choose option A, thinking that more complexity will capture more patterns, but this actually worsens overfitting and further degrades performance on new data.
Expert Analysis
Diagnosing the Problem
The scenario describes a classic case of overfitting: the model has memorized the training dataset, including its noise and outliers, but fails to generalize to new, unseen data. High training accuracy combined with poor performance on new data is the hallmark of this issue.
Why Option B is Correct
Increasing the regularization parameter adds a stronger penalty for model complexity (e.g., large weights in linear models or deep trees). This forces the model to learn simpler, more general patterns rather than fitting every detail of the training data. As a result, the model's ability to generalize to new data improves significantly.
Why Other Options Are Wrong
- Option A suggests decreasing the regularization parameter, which would make the model even more complex and exacerbate overfitting.
- Option C (adding more features) increases the dimensionality of the input, which can lead to even more overfitting unless carefully managed with feature selection or dimensionality reduction.
- Option D (training for more epochs) allows the model to fit the training data even more tightly, again worsening overfitting rather than resolving it.
Community Consensus
All community voters agreed on Option B, noting that regularization is the standard technique to reduce overfitting and improve generalization.
Official Reference
https://docs.aws.amazon.com/machine-learning/latest/dg/handling-overfitting.html
This link opens the official vendor documentation in a new tab.
Exam Strategy
When a question describes good training performance but poor new-data performance, immediately think 'overfitting.' Then look for solutions that reduce model complexity: regularization, pruning, early stopping, or less training.
Community Votes
100% of anonymous learners picked answer B. Votes are pick records left by other test-takers — they are not the verified answer.
Discussion 4 contributions
Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.
From public community threads 4
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26b8fe1 👍 2 Answered BIncrease the regularization parameter to decrease model complexity. Increasing the regularization parameter helps prevent overfitting by penalizing more complex models, encouraging the model to generalize better to new data. Would you like more detailed information on how to implement this change or any other aspect of model tuning?
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aws_Tamilan 👍 1 Answered BThe most effective solution to resolve overfitting and improve the model’s performance on new data is B. Increase the regularization parameter. This helps make the model simpler, reducing the likelihood of overfitting and improving its ability to generalize.
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may2021_r 👍 1 Answered BThe correct answer is B. Increasing the regularization parameter reduces model complexity and prevents overfitting.
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Jessiii 👍 1 Answered BReduces overfitting, improving generalization to new data.
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