input: 
  def categorize_dataset(records: list) -> str:
      """ Create a machine learning algorithm to categorize a dataset of records.
      The dataset is given as a list of dictionaries where each dictionary contains 
      a label and corresponding features. The function will train a Support Vector 
      Classifier (SVC) and return the predicted 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
      
      X = []
      y = []
      for data in records:
          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:     
      import numpy as np
      from sklearn.svm import SVC
      
      X = []
      y = []
      for data in records:
          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]
  