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Related Questions
- What are some common metrics used to evaluate model performance, and how can they indicate overfitting or underfitting?
- How can a model's training and validation accuracy be used to diagnose overfitting or underfitting?
- What are some techniques for regularizing a model to prevent overfitting, such as L1 and L2 regularization, dropout, and early stopping?
- What are some methods for dealing with underfitting, such as increasing the model's capacity, collecting more data, or using transfer learning?
- How can a model's performance on a test set be used to evaluate its generalizability and identify potential overfitting or underfitting issues?
- What are some common signs of overfitting, such as high training accuracy and low validation accuracy, and how can they be addressed?
- How can the use of cross-validation be used to evaluate a model's performance and detect overfitting or underfitting?
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