Sampling

Bootstrap sampling is a widely used method in machine learning and in statistics. The main idea is that we try to decrease overfitting and the chance of myopic tree-building if run our algorithm multiple times using the same data, but always taking a different sample with repetitions from our original data. (For instance, random forest builds the trees using repeated bootstrap samples.) On a machine learning class one of my class mates asked what percentage of the original data shows up in the bootstrapped sample.