input: 
  from typing import List, Dict
  from sklearn.svm import SVC
  
  def categorize_dataset(records: List[Dict[str, object]]) -> str:
      """
      Categorizes a dataset of records using a machine learning algorithm.
      Each record contains a label and a list of features.
      
      >>> 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'
      """
      X = []
      y = []
      for record in records:
          X.append(record['features'])
          y.append(record['label'])
      
      clf = SVC()
      clf.fit(X, y)
      
      predict_data = [1, 0, 1]
      predicted_label = clf.predict([predict_data])
      
      return predicted_label[0]
  
output:     X = []
      y = []
      for record in records:
          X.append(record['features'])
          y.append(record['label'])
      
      clf = SVC()
      clf.fit(X, y)
      
      predict_data = [1, 0, 1]
      predicted_label = clf.predict([predict_data])
      
      return predicted_label[0]