I’m Building a Neural Network for Signal Classification, but the Model Overfits - How Can I Resolve This? #6453
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I’m currently developing a neural network to classify signal data, but I’m facing an overfitting issue. The model achieves over 98% accuracy on training data but drops to around 70% on validation. I’ve already tried reducing the number of layers and using dropout, but the problem persists. Could this be due to an imbalance in the dataset or insufficient data augmentation? What advanced techniques—such as L2 regularization, batch normalization, or early stopping—would be most effective in this context? |
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If your neural network is overfitting, it likely means it performs well on training data but poorly on unseen data. This can be addressed by applying regularization techniques such as L2 regularization, dropout (e.g., 0.3–0.5 dropout rate), or early stopping. Additionally, reducing model complexity or augmenting the dataset can help improve generalization. For example, using batch normalization and increasing training data through synthetic generation often reduces overfitting in signal-based models by 20–30% in validation error. |
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If your neural network is overfitting, it likely means it performs well on training data but poorly on unseen data. This can be addressed by applying regularization techniques such as L2 regularization, dropout (e.g., 0.3–0.5 dropout rate), or early stopping. Additionally, reducing model complexity or augmenting the dataset can help improve generalization. For example, using batch normalization and increasing training data through synthetic generation often reduces overfitting in signal-based models by 20–30% in validation error.