How does the regularization parameter affect the performance of a neural network?

The regularization parameter plays a crucial role in determining the performance of a neural network. By controlling the trade-off between fitting the training data and preventing overfitting, it helps to achieve a good balance in model complexity. A higher regularization parameter value increases the penalty for complex models, reducing the risk of overfitting. This leads to a more generalized model that performs well not only on the training data but also on unseen data. On the other hand, a lower regularization parameter allows the model to fit the training data more closely, which may result in a higher risk of overfitting. Therefore, finding an optimal regularization parameter is critical to strike the right balance between model flexibility and generalization ability.

This mind map was published on 20 August 2023 and has been viewed 93 times.