Explain the concept of learning curves and how they can be used to diagnose whether a model is suffering from overfitting or underfitting, or if more data might be helpful.
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what is overfitting Informational
- What role does pre-training play in mitigating overfitting in large language models?
- Explain how ensemble methods like bagging (e.g., random forests) and boosting (e.g., gradient boosting) can help in reducing overfitting compared to using a single model.
- What are some ethical considerations related to deploying models that might be overfit, especially in high-stakes applications?
- What are some specific regularization techniques used in transformer models for NLP?
- What is exploration and exploitation trade-off and its relation to overfitting in RL?
- What are some best practices for building and deploying machine learning systems that are robust to overfitting and that generalize well in real-world settings?
- How should you report the presence and extent of overfitting when evaluating and communicating the performance of a machine learning model?
- How does transfer learning potentially mitigate overfitting?
- How does setting a minimum number of samples required at a leaf node help prevent overfitting?
- Consider the bias-variance trade-off. How do bagging and boosting typically affect the bias and variance of the resulting ensemble model compared to the individual base models?
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