How does the PaddlePaddle framework address the issue of overfitting?

The PaddlePaddle framework offers methods to address overfitting issues.

1. Data augmentation involves randomly rotating, cropping, scaling, and other operations on training data to increase diversity and reduce the risk of overfitting.
2. Regularization: The PaddlePaddle framework supports the use of methods such as L1 regularization and L2 regularization during the model training process, which helps prevent overfitting by penalizing the complexity of the model.
3. PaddlePaddle framework offers Dropout feature, which randomly sets a portion of neuron outputs to 0 during model training to reduce neural network complexity and prevent overfitting.
4. Stop training early: monitor the performance metrics on the validation set during training, and stop training when the validation set performance starts to decrease to prevent overfitting.
5. Model ensemble: By integrating multiple different models, the risk of overfitting can be reduced. The PaddlePaddle framework provides a convenient interface for achieving model ensemble.