title: Bootstrap Sampling on Large Data
body: <p>I have a large dataset and I am trying to estimate function f(x) for all instances in that dataset. Which of the following approach is better?</p>
  
  <p><strong>Approach 1:</strong> Sampling N instances from the dataset and use bootstrapping for these N instances to estimate f(x).</p>
  
  <p><strong>Approach 2:</strong> M times sample N instances for the large dataset. Then calculate f(x) for each of these M sample cases, then aggregate (for example: average) the result.</p>