title: Plot f1 for a specific class with sklearn's learning_curve() rather than the weighted f1 scores
body: <p>When plotting a learning curve with <code>sklearn.model_selection.learning_curve()</code> on a boolean supervised classifier, it defaults to displaying the weighted f1 score.</p>
  
  <p>But I'd like to plot the f1 score for a specific class. In this case the positive (aka: 1) class.</p>
  
  <p>In the context of below (from <code>sklearn.metrics.classification_report</code>), its plotting <code>avg / total</code>, but I would like to plot metrics for the class <code>1</code>.</p>
  
  <p><a href="https://i.stack.imgur.com/qcuMY.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/qcuMY.png" alt="enter image description here"></a></p>
  
  <p><strong>Plot</strong></p>
  
  <p><a href="https://i.stack.imgur.com/nWVvy.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/nWVvy.png" alt="enter image description here"></a></p>
  
  <p><strong>Code</strong></p>
  
  <pre><code>...
  estimator = classifier_class()
  cv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)
  train_sizes, train_scores, test_scores = learning_curve(estimator, X_recombined, y_recombined, cv=cv) # n_jobs=n_jobs, train_sizes=train_sizes
  
  train_scores_mean = np.mean(train_scores, axis=1)
  train_scores_std = np.std(train_scores, axis=1)
  test_scores_mean = np.mean(test_scores, axis=1)
  test_scores_std = np.std(test_scores, axis=1)
  plt.grid()
  
  plt.fill_between(train_sizes, 
                   train_scores_mean - train_scores_std,
                   train_scores_mean + train_scores_std, 
                   alpha=0.1, color="r")
  
  plt.fill_between(train_sizes, 
                   test_scores_mean - test_scores_std,
                   test_scores_mean + test_scores_std, 
                   alpha=0.1, color="g")
  
  plt.plot(train_sizes, train_scores_mean, 'o-', color="r", label="Training score")
  
  plt.plot(train_sizes, test_scores_mean, 'o-', color="g", label="Cross-validation score")
  
  plt.legend(loc="best")
  </code></pre>
  
  <p>Is this possible?</p>