How to calibrate the probabilities predicted by a model to reflect the true uncertainty?
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what is overfitting Informational
- How does the complexity of the decision boundary relate to overfitting?
- What role does self-supervised learning play in learning representations that generalize well?
- Consider a support vector machine (SVM) with a non-linear kernel. How can choosing a kernel that is too complex lead to overfitting?
- Explain the concept of early stopping in the context of training iterative models like neural networks. Why does it help prevent overfitting?
- Consider a random forest model. Why is it generally less prone to overfitting than a single decision tree?
- How does overfitting manifest in clustering algorithms?
- How can the quality of the training data affect overfitting? For example, how might noisy labels or biased samples influence the model's generalization ability?
- You are training a deep neural network, and you observe that the training loss is decreasing steadily, but the validation loss starts to increase after a certain number of epochs. What phenomenon are you observing, and how can you address it?
- What is the relationship between bias and variance in the context of overfitting?
- Consider convolutional neural networks (CNNs). What are some specific regularization techniques used to prevent overfitting, such as dropout and batch normalization?
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