input: 
  def categorize_dataset(training_data: list) -> str:
      """
      Create a machine learning algorithm in Python to categorize a dataset of records. The algorithm uses an SVM classifier.
      
      >>> 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 training_data:
          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 training_data:
          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]