input: def categorize_dataset(dataset):
      """Implement a machine learning algorithm to categorize a given dataset of records.
      The dataset is a list of dictionaries where each dictionary contains a 'label' and 'features'.
      Use a Support Vector Classifier (SVC) from scikit-learn.
      
      Example:
      >>> dataset = [
      ...    {"label": "apple", "features": [1, 1, 0]}, 
      ...    {"label": "banana", "features": [0, 1, 1]}, 
      ...    {"label": "lemon", "features": [0, 0, 1]}, 
      ...    {"label": "orange", "features": [1, 0, 0]}, 
      ...    {"label": "pear", "features": [0, 1, 0]}, 
      ... ]
      >>> categorize_dataset(dataset)
      'banana'
      """
  
      from sklearn.svm import SVC
      
      X = []
      y = []
      for data in dataset:
          X.append(data['features'])
          y.append(data['label'])
      
      clf = SVC()
      clf.fit(X, y)
      
      predict_data = [1, 0, 1]
      predicted_label = clf.predict([predict_data])
      
      return predicted_label[0]
output:     from sklearn.svm import SVC
      
      X = []
      y = []
      for data in dataset:
          X.append(data['features'])
          y.append(data['label'])
      
      clf = SVC()
      clf.fit(X, y)
      
      predict_data = [1, 0, 1]
      predicted_label = clf.predict([predict_data])
      
      return predicted_label[0]