where the blue lines is the metrics obtained with training set and red lines with validation set
Is there anything I can infer from the fact that the accuracy on the training sets is really high (almost 1) ?
From what I understand, it means that the complexity of my model is enough / too big. But does it means my model could theoretically reach such a score on validation set with same dataset and appropriate hyperparameters ? With same hyperparameters but bigger dataset ?
My question is not how to avoid overfitting.