If my validation error is smaller than my training error, what does this imply? Does this mean the model is 'underfit'?
A model is considered to be 'underfitting' when both the training and validation error are high. When your validation error is lower than your training error than the fit is obscure and inconclusive.
There are multiple reasons why this can happen but one can't be completely certain. It could be because the test cases during the validation belonged to the type of data that influenced the model the most while training or maybe because the test cases contain the type of data that your model is good at predicting. If there is a very small difference, it could be random.
Although, based on your question it is difficult to comment more specifically without knowing the train-test split method or the evaluation metric.
Regarding underfitting and overfitting you can keep these in mind:
- Underfitting: High Validation and Training Error
- Overfitting: High Validation Error and Low Training Error