title: sklearn learning curve for classification with varying number of sample from one class only
body: <p>Using sklearn's learning_curve for a binary classification, is there a straightforward way to instead of using different sample sizes from the whole training set (class 0 + class 1), use different sample sizes from one class only (class 1)? So, explore how accuracy changes while class 0 is held stable, but we increase the sample size of class 1 in the training data.</p>
  <p>So for example <a href="https://scikit-learn.org/stable/auto_examples/model_selection/plot_learning_curve.html" rel="nofollow noreferrer">here</a> instead of number of training examples, the x axis will show the number of training example from class 1?</p>