



What is Bayesian model averaging and how does it help with overfitting?
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what is overfitting Informational
- How does overfitting affect the uncertainty estimation of a model?
- When monitoring a deployed machine learning model over time, what are some signs that the model might be starting to overfit to new data or experiencing concept drift?
- Are there any regulatory or compliance requirements related to model overfitting in specific industries (e.g., finance, healthcare)?
- Is there a point of diminishing returns when adding more training data? When might adding more data not significantly reduce overfitting?
- What are some promising new approaches for addressing the challenges of overfitting in increasingly complex models and datasets?
- Can a loss function that is too sensitive to outliers lead to overfitting?
- How can we ensure that the distilled model retains the generalization capabilities of the teacher model?
- Explain the process of boosting. How does sequentially training models to correct the errors of previous models and combining them lead to improved performance and potentially reduced overfitting?
- Explain the concept of early stopping in the context of training iterative models like neural networks. Why does it help prevent overfitting?
- Looking at misclassified examples what patterns might suggest overfitting?
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