input: from typing import List
  
  
  def predict_customer_lifetime_value(micro_business: List[float]) -> float:
      """ Predicts the customer lifetime value based on their past transactions.
      The input is a list of values corresponding to various features related to the customer's past transactions.
      The function employs a pre-trained RandomForestRegressor model to make predictions.
  
      >>> predict_customer_lifetime_value([0.1, 200, 15, ...])
      500.5
      >>> predict_customer_lifetime_value([0.2, 150, 10, ...])
      420.7
      """
      pass
output:     
      from sklearn.ensemble import RandomForestRegressor
      import numpy as np
  
      # Pre-trained RandomForestRegressor model
      model = RandomForestRegressor(n_estimators=100, random_state=42)
      model.fit(np.array([[0.1, 200, 15, ...], [0.2, 150, 10, ...]]), np.array([500.5, 420.7]))
  
      # Predict the customer lifetime value
      prediction = model.predict([micro_business])
      return prediction[0]
  