code: def make_prediction(dataset=None, binary=False):
      """Make some classification predictions on a toy dataset using a SVC
      If binary is True restrict to a binary classification problem instead of a
      multiclass classification problem
      """
  
      if dataset is None:
          # import some data to play with
          dataset = datasets.load_iris()
  
      X = dataset.data
      y = dataset.target
  
      if binary:
          # restrict to a binary classification task
          X, y = X[y < 2], y[y < 2]
  
      n_samples, n_features = X.shape
      p = np.arange(n_samples)
  
      rng = check_random_state(37)
      rng.shuffle(p)
      X, y = X[p], y[p]
      half = int(n_samples / 2)
  
      # add noisy features to make the problem harder and avoid perfect results
      rng = np.random.RandomState(0)
      X = np.c_[X, rng.randn(n_samples, 200 * n_features)]
  
      # run classifier, get class probabilities and label predictions
      clf = svm.SVC(kernel="linear", probability=True, random_state=0)
      probas_pred = clf.fit(X[:half], y[:half]).predict_proba(X[half:])
  
      if binary:
          # only interested in probabilities of the positive case
          # XXX: do we really want a special API for the binary case?
          probas_pred = probas_pred[:, 1]
  
      y_pred = clf.predict(X[half:])
      y_true = y[half:]
  
      return y_true, y_pred, probas_pred
  
imports: from functools import partial
  import numpy
  import pytest
  from sklearn import datasets, svm
  from sklearn.metrics import accuracy_score, average_precision_score, brier_score_loss, cohen_kappa_score, jaccard_score, precision_score, recall_score, roc_auc_score
  from sklearn.preprocessing import label_binarize
  from sklearn.utils._testing import assert_allclose, assert_array_equal, assert_no_warnings
  from sklearn.utils.validation import check_random_state
  from imblearn.metrics import classification_report_imbalanced, geometric_mean_score, macro_averaged_mean_absolute_error, make_index_balanced_accuracy, sensitivity_score, sensitivity_specificity_support, specificity_score
label_desc: Use image node_id 1 for calling a global function with example usage: make_prediction(dataset, binary) and returns: y_true,  y_pred,  probas_pred