title: How does make_scorer work when using custom loss with predicted probabilities in GridSearchCV scoring?
body: <p>I've been using a custom loss function as the scoring method in <code>GridSearchCV</code>.
  My dataset is binary classified and I'm predicting probabilities from it, that's why I've set <code>needs_proba=True</code> in <code>make_scorer</code>.</p>
  <pre><code>def custom_loss(y_true, y_pred):
      return mean_squared_log_error(y_true, y_pred, squared=False)
  
  model = GridSearchCV(model_pipe, 
                       param_grid=param_grid, 
                       cv=StratifiedKFold(n_splits=5, random_state=73, shuffle=True), 
                       n_jobs=-1, 
                       scoring=make_scorer(custom_loss, greater_is_better=True, needs_proba=True),
                       refit=True,
          )
  </code></pre>
  <p>In <code>make_scorer</code> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html" rel="nofollow noreferrer">doc</a> there's the following description about <code>needs_proba</code>:</p>
  <blockquote>
  <p>If True, for binary y_true, the score function is supposed to
  accept a 1D y_pred (i.e., probability of the positive class, shape
  (n_samples,)).</p>
  </blockquote>
  <p>This part: &quot;the score function is supposed to accept a 1D y_pred&quot; is not completely clear for me.</p>
  <p>Considering <code>y_true</code> is a 1D array ([1 0 0]) and <code>y_pred</code> is a 2D probabilities array ([0.9 0.1], [0.2 0.8], [0.3 0.7]) does <code>make_scorer</code> automatically considers the positive probabilities from <code>y_pred</code> (<code>y_pred[:,1]</code>) to call the <code>custom_loss</code> function and compare them to <code>y_true</code>?</p>
  <p>Thanks!</p>