input: 
  def categorize_dataset(records: List[Dict[str, Any]]) -> str:
      """ Categorize a dataset of records using a machine learning algorithm. Each record contains a 'label' and a 'features' list. The features are used to train a support vector classifier (SVC). The classifier is then used to predict the label for a new set 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'
      """
      import numpy as np
      from sklearn.svm import SVC
  
      # Feature extraction
      X = []
      y = []
      for data in records:
          X.append(data['features'])
          y.append(data['label'])
  
      # Model
      clf = SVC()
      clf.fit(X, y)
  
      # Prediction
      predict_data = [1, 0, 1]
      predicted_label = clf.predict([predict_data])
  
      return predicted_label[0]
  
output:     import numpy as np
      from sklearn.svm import SVC
  
      # Feature extraction
      X = []
      y = []
      for data in records:
          X.append(data['features'])
          y.append(data['label'])
  
      # Model
      clf = SVC()
      clf.fit(X, y)
  
      # Prediction
      predict_data = [1, 0, 1]
      predicted_label = clf.predict([predict_data])
  
      return predicted_label[0]