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This study investigates the impact of hyperparameters on overfitting in deep learning models for predicting breast cancer metastasis using clinical data. The researchers found that specific hyperparameters, such as learning rate, decay, and batch size, significantly influence both overfitting and model performance, and that hyperparameter tuning can enhance prediction accuracy. The results highlight the importance of understanding hyperparameter interactions to mitigate overfitting in deep learning applicat

Author

Egar Firmansyah

Language

EN