How to prevent underfitting

Increasing the model complexity
Your model may be underfitting simply because it is not complex enough to capture patterns in the data. Using a more complex model, for instance by switching from a linear to a non-linear model or by adding hidden layers to your neural network, will very often help solve underfitting.

Reducing regularization
The algorithms you use include by default regularization parameters meant to prevent overfitting. Sometimes, they prevent the algorithm from learning. Reducing their values generally helps.

Adding features to training data
In contrast to overfitting, your model may be underfitting because the training data is too simple.
It may lack the features that will make the model detect the relevant patterns to make accurate prediction. Adding features and complexity to your data can help overcome underfitting.