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Related Questions
- Can overfitting lead to poor generalization on data points that were not observed during training, and conversely, how does overfitting impact out-of-sample performance?
- Under what conditions can an underfitted model lead to inconsistent predictions on new data points not seen during training, particularly in scenarios with limited sample sizes?
- Why do overfitting models tend to overestimate training performance, which can sometimes result in high variance performance on unseen inputs?
- How do regularization methods, such as L2 regularization, affect the sensitivity of overfitting issues on out-of-distribution inputs?
- Given that underfitting manifests as high bias, will increasing model capacity or model complexity always reduce underfitting issues in AI performance on new inputs?
- Can ensembling machine learning models (e.g., bagging, stacking) help alleviate issues due to overfitting in AI models trained on different subsets of the data distribution?
- What aspects of model selection and dataset curation play a decisive role in ensuring that well-performing models on held-out data do not encounter significant drops in performance during deployment on novel inputs, particularly when these inputs derive from unseen environments?
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