A learning curve is a useful diagnostic graphic that depicts the behavior of your machine learning algorithm (your hypothesis) with respect to the available quantity of observations. The idea is to compare how the training performance (the error or accuracy of the in-sample cases) behaves with respect to the cross-validation (usually tenfold) using different in-sample sizes.
As far as the training error is concerned, you should expect it to be high at the start and then decrease. However, depending on the bias and variance level of the hypothesis, you will notice different behaviors:
- A high-bias hypothesis tends to start with average error performances, decreases rapidly on being exposed to more complex data, and then remains ...
