Dropout prevents a neural network ____________.1 pointfrom overfittingfrom underfittingfrom ideal fit

Understanding Dropout in Neural Networks

1. Definition of Dropout: Dropout is a regularization technique used in neural networks to prevent overfitting. It works by randomly setting a fraction of the input units to zero during training, which helps in making the model robust and prevents it from learning noise in the training data.

2. Overfitting: Overfitting occurs when a model learns the training data too well, including its noise and outliers, leading to poor generalization on new, unseen data.

3. Underfitting: Underfitting happens when a model is too simple to capture the underlying patterns in the data, resulting in poor performance on both training and test datasets.

4. Ideal Fit: An ideal fit would represent a model that generalizes well, striking a balance between underfitting and overfitting.

Conclusion

Given the role of dropout in a neural network, the correct answer is: Dropout prevents a neural network from overfitting.