Cures for Underfitting
To prevent underfitting, we need to ensure the model complexity.

The first method that comes to mind is to obtain more training data. However, this is not an easy task for most problems. In such cases, we can bring data augmentation into service. So, we can increase the amount of data available by creating slightly modified synthetic copies of the data points at hand.

Similarly, increasing the number of passes on the training data is a viable approach for iterative algorithms. Increasing the number of epochs in a neural network is a well-known practice to ensure model fitting.