There are several strategies to prevent overfitting in cancer research:
1. Simplifying the Model: Using simpler models with fewer parameters can reduce the risk of overfitting. 2. Regularization: Techniques like Lasso and Ridge regularization add a penalty for large coefficients, thus discouraging the model from becoming too complex. 3. Data Augmentation: Increasing the size of the training dataset through techniques like data augmentation can help. In the case of imaging data, this can include transformations like rotation, scaling, and flipping of images. 4. Early Stopping: Monitoring the model's performance on a validation set and stopping training when performance starts to degrade can prevent overfitting. 5. Dropout: In neural networks, dropout involves randomly setting a fraction of input units to zero during training, which prevents the network from becoming too reliant on specific nodes.
