What are some common pitfalls to avoid when trying to prevent overfitting, such as using the test set for hyperparameter tuning?
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what is overfitting Informational
- How does overfitting affect the performance of language models on unseen text?
- How does the number of features compared to the number of data points affect the likelihood of overfitting in linear regression?
- What is the no free lunch theorem and its implications for overfitting?
- How do tree-based models like decision trees and random forests overfit?
- What role does self-supervised learning play in learning representations that generalize well?
- You have trained two models for the same task. Model A is more complex and achieves higher accuracy on the training data than Model B, which is simpler. However, Model B performs better on the validation data. What does this suggest about overfitting?
- What are the potential societal impacts of deploying overfit models?
- Describe some techniques for visualizing the decision boundaries of a model. How might the complexity of these boundaries provide insights into whether the model is overfitting?
- How to set up alerts and triggers to flag potential issues like overfitting in production?
- What are some techniques for reducing the memory footprint and computational cost of large deep learning models without sacrificing generalization performance?
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