Isn't it crucial to understand overfitting in machine learning, especially when developing models for predicting cryptocurrency trends? Overfitting can severely undermine the reliability of our predictions. What strategies can we implement to prevent this issue and ensure our models remain robust and accurate in the volatile crypto market?
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