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Related Questions
- How do L1 and L2 regularization compare in terms of avoiding overfitting, and under what conditions might you prefer one over the other?
- What effect does increasing the regularization strength have on the model's capacity to fit the training data, and what is the optimal balance to avoid overfitting?
- Can you explain the difference between L1 regularization and L2 regularization, and how they influence model weights and feature importance?
- How do regularization strength and L1/L2 ratio impact model complexity and generalizability on unseen data?
- In what scenarios is a high L1 ratio preferable to a high L2 ratio, and vice versa?
- Can you describe the relationship between overfitting, underfitting, and regularization in terms of model bias-variance tradeoff?
- How does adjusting regularization hyperparameters affect model accuracy and interpretability in scenarios with high-dimensional features and few samples?
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