input: 
  def categorize_dataset(training_data):
      """
      This function takes a dataset of records and categorizes new data based on the trained model using a Support Vector Classifier (SVC).
      Each record in the dataset contains a label and a list of features. The function trains the SVC on the given dataset and then predicts
      the label for a new data point.
      
      >>> categorize_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]}, 
      ... ])
      'banana'
      """
  
output:     from sklearn.svm import SVC
      from sklearn.model_selection import train_test_split
  
      labels = [record['label'] for record in training_data]
      features = [record['features'] for record in training_data]
  
      X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)
  
      svc_model = SVC()
      svc_model.fit(X_train, y_train)
  
      new_data_point = [0, 1, 1]
      predicted_label = svc_model.predict([new_data_point])
  
      return predicted_label[0]