Does my model overfits or underfits based on the learning curves I obtain?

Question

I want to find a good fit for my data, so I started with training the basic model (for the simple binary classification problem) and plotted the learning curves from the training. The plot I got:

Learning curves for training of my model

However, I am not sure what these curves mean, does the model overfit or underfit? Seems like there is a quite big gap between the training and validation curve, so I assumed it's overfitting and I added some regularization, however that seemed to only decrease both validation and test set accuracy and the gap remained more-less the same. So is this plot showing bias then?

Answer 1

When both curves have reached a plateau, are close and fairly high then it's underfitting .

When both curves are separated by substantial gap and model performs significantly better on training data ( training curve is fairly low ) than on validation curve (fairly high in graph) then it's overfitting .

Ideally, you'd want both curves to stay low in the graph and very close to each other with not much gap in between.