What are some common mistakes in AI validation?
Some common mistakes include not using a diverse dataset, overfitting, and neglecting to validate in real-world scenarios.
Can you explain why overfitting is a problem?
Overfitting occurs when a model learns the training data too well, including noise, making it perform poorly on unseen data.
How can I avoid these mistakes?
To avoid these mistakes, ensure you use a representative dataset, apply regularization techniques, and validate your model in real-world conditions.
What is regularization?
Regularization is a technique used to prevent overfitting by adding a penalty for larger coefficients in the model.
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