input: from typing import List, Dict
  import numpy as np
  from sklearn.svm import SVC
  
  def categorize_dataset(dataset: List[Dict[str, list]]) -> str:
      """ Trains a machine learning model on a given dataset of records where each record
      contains a label and a list of features. The model is then used to categorize a new
      record. The output is the predicted label for the new record.
      >>> 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]},
      ])
      'lemon'
      """
  
      training_data = dataset
      X = []
      y = []
      for data in training_data:
          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:     training_data = dataset
      X = []
      y = []
      for data in training_data:
          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]