Mila wishes to build a machine learning model to classify PET images as either containing cancerous tumours, or not. Mila chooses to use decision trees as her base model, and performs the following steps:

·        First, she splits her data into training/test sets.

·        Next, Mila creates 100 subsets of her training data by resampling images from the training data, with replacement, until each dataset contains 200 images.

·        Then, for each subset, she chooses to only look at a random sample of 40 pixels (same pixels for images in the same subset)

·        Then, she trains 100 models (one for each subset)

·        Finally, Mila runs each of the 100 models through the test set, and uses as her final prediction for each test image the majority vote of the 100 models.

Mila’s experiment is an example of the ensemble method known as: