About learning curves
If the gap between the training and cross-validation accuracy is increasing then this is an indication that your model is overfitting on the training data.
With every iteration (supplying additional training data) your model is better able to capture the training data, however it is no longer able to better generalise (and thus the cross-validation accuracy converges).
I am trying to plot the learning curves for my SVC classifier with sklearn.learning_curve. From the plot, I find that both of my training scores and test scores increases simultaneously. But the gap between the training curve and cross-validation curve becomes larger with the increasing number of the samples. As I know, the training scores should decrease when more samples supplied. Do you guys have any sense about this problem?