I would like to clarify my understanding of learning curves with two example plots below. I am experimenting with small data sets here between 500 and 1500 samples to clarify my understanding.
My understanding from the learning curve below is that underlying data must have a lot of noise and therefore the learning algorithm is not able to generalize the underlying function. This learning curve indicates neither high bias or variance. Just the overall data samples are not a good indicator of the outcome. Is my interpretation of this learning curve above correct? Would getting more data help?
From the learning curve below, I am interpreting this as a good learning curve. Because both the training and cross validation misclassification errors are going down as we add more samples. Around ~240 samples we get the least difference between the training and validation sets. Is this indicative of high variance since the training and validation never converge even though the misclassification error delta is pretty low between the two?