How does the regularization parameter (C) in SVMs control the trade-off between fitting the training data and preventing overfitting?
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what is overfitting Informational
- Explain how transfer learning can be an effective strategy for reducing overfitting, especially when you have limited data for the target task but access to a large dataset for a related task.
- Explain the concept of data leakage and why it is crucial to avoid it when training and evaluating machine learning models to get a realistic assessment of overfitting.
- How does overfitting affect the reliability and trustworthiness of machine learning models in critical applications?
- How does fine-tuning of pre-trained models need to be done carefully to avoid overfitting?
- What are some advanced techniques for assessing the generalization ability of a model beyond simple train-validation splits, such as bootstrapping and permutation tests?
- How does the use of regularization techniques like L1 and L2 weight decay help prevent overfitting in neural networks?
- What are some common weight initialization strategies?
- Consider recurrent neural networks (RNNs). What are some specific challenges related to overfitting in RNNs, especially with long sequences?
- How does the choice of model architecture introduce an inductive bias?
- Is there a point of diminishing returns when adding more training data? When might adding more data not significantly reduce overfitting?
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