input: from typing import List, Dict
  
  def categorize_dataset(dataset: List[Dict[str, any]]) -> str:
      """
      Using a machine learning algorithm, categorize a given dataset of records.
      Each record in the dataset contains a label and a list of features.
      This function uses a Support Vector Classifier (SVC) from sklearn to train a model on the provided dataset and then make a prediction on new data.
  
      >>> 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]}
      ... ]
      >>> categorize_dataset(dataset)
      'banana'
      """
      import numpy as np
      from sklearn.svm import SVC
  
      # Feature extraction
      X = []
      y = []
      for data in dataset:
          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 dataset:
          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]