input: def categorize_dataset(training_data: list) -> str:
      """ Create a machine learning algorithm in Python to categorize a dataset of records. 
      The dataset is a list of dictionaries where each dictionary has a 'label' key 
      and a 'features' key. The 'label' is a string representing the category and 
      the 'features' is a list of integers representing the features. The function should 
      train a Support Vector Machine (SVM) classifier using the dataset and return the 
      predicted label for a given set of features.
      >>> training_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]}, 
                          ]
      >>> categorize_dataset(training_data)
      'banana'
      """
output:     from sklearn import svm
      from sklearn.model_selection import train_test_split
  
      labels = [record['label'] for record in training_data]
      features = [record['features'] for record in training_data]
  
      X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)
  
      clf = svm.SVC()
      clf.fit(X_train, y_train)
  
      test_features = [0, 1, 1]
      predicted_label = clf.predict([test_features])
  
      return predicted_label[0]