Reducing Bias (to prevent underfitting)

• Increase Model Complexity:

• Use more complex models, such as deep neural networks, or add more layers and neurons to existing models.

• Use models that capture non-linear relationships (e.g., decision trees, random forests, or support vector machines with non-linear kernels).

• Feature Engineering:

• Add more relevant features that may capture the underlying patterns in the data.

• Transform features to better represent the data (e.g., polynomial features, interaction terms).

• Increase Training Time:

• Train the model for more epochs, especially for deep learning models, to allow them to learn more complex patterns.

Reducing Variance (to prevent overfitting)

• Simplify the Model:

• Use a simpler model with fewer parameters to avoid overfitting the training data.

• Reduce the number of layers or units in a neural network, or prune a decision tree.

• Increase Training Data:

• Collect more data to give the model more examples, helping it generalize better to unseen data.

• Regularization Techniques:

• Augment the existing dataset with transformations (for images, techniques like rotation, flipping, and cropping).
• Split into training and test data sets multiple times

• Use L1/L2 Regularization: Adds a penalty term to the loss function, discouraging the model from fitting noise.

• Use Dropout (for neural networks): Randomly drop neurons during training to prevent the model from becoming too dependent on specific pathways.

• Use Early Stopping: Stop training when performance on a validation set starts to degrade, indicating overfitting.