input: from typing import List, Dict
  
  def categorize_data(training_data: List[Dict[str, any]]) -> str:
      """ Categorize a dataset of records using a Support Vector Classifier from Scikit-learn.
      The function takes in a list of dictionaries where each dictionary contains a label and features.
      It trains the model on the training data and predicts the label for a new set of features.
      >>> categorize_data([
      >>> {"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'
      """
output:     from sklearn.svm import SVC
      from sklearn.preprocessing import LabelEncoder
  
      labels = [item['label'] for item in training_data]
      features = [item['features'] for item in training_data]
  
      label_encoder = LabelEncoder()
      encoded_labels = label_encoder.fit_transform(labels)
  
      model = SVC()
      model.fit(features, encoded_labels)
  
      new_features = [0, 1, 1]
      predicted_label = model.predict([new_features])
  
      return label_encoder.inverse_transform(predicted_label)[0]