In (high) school we sit together with other students, and learn together, but we are not supposed to work together during the exam. The reason is, of course, that teachers want to know what we have learned, and if we just copy exam answers from friends, we may have not learned anything. Later in life we discover that teamwork is important. For example, this book is the product of a whole team, or possibly a group of teams.

Clearly a team can produce better results than a single person. However, this goes against Occam's razor, since a single person can come up with simpler theories compared to what a team will produce. In machine learning we nevertheless prefer to have our models cooperate with the following schemes:

• Bagging
• Boosting
• Stacking
• Blending
• Voting and averaging

Bootstrap aggregating or bagging is an algorithm introduced by Leo Breiman in 1994, which applies Bootstrapping to machine learning problems. Bootstrapping is a statistical procedure, which creates datasets from existing data by sampling with replacement. Bootstrapping can be used to analyze the possible values that arithmetic mean, variance, or another quantity can assume.

The algorithm aims to reduce the chance of overfitting with the following steps:

1. We generate new training sets from input train data by sampling with replacement.
2. Fit models to each generated training set.
3. Combine the results of the models by averaging or majority voting.

In the context of supervised learning we define weak learners as learners that are just a little better than a baseline such as randomly assigning classes or average values. Although weak learners are weak individually like ants, together they can do amazing things just like ants can. It makes sense to take into account the strength of each individual learner using weights. This general idea is called boosting. There are many boosting algorithms; boosting algorithms differ mostly in their weighting scheme. If you have studied for an exam, you may have applied a similar technique by identifying the type of practice questions you had trouble with and focusing on the hard problems.

Face detection in images is based on a specialized framework, which also uses boosting. Detecting faces in images or videos is a supervised learning. We give the learner examples of regions containing faces. There is an imbalance, since we usually have far more regions (about ten thousand times more) that don't have faces. A cascade of classifiers progressively filters out negative image areas stage by stage. In each progressive stage, the classifiers use progressively more features on fewer image windows. The idea is to spend the most time on image patches, which contain faces. In this context, boosting is used to select features and combine results.

Stacking takes the outputs of machine learning estimators and then uses those as inputs for another algorithm. You can, of course, feed the output of the higher-level algorithm to another predictor. It is possible to use any arbitrary topology, but for practical reasons you should try a simple setup first as also dictated by Occam's razor.

Blending was introduced by the winners of the one million dollar Netflix prize. Netflix organized a contest with the challenge of finding the best model to recommend movies to their users. Netflix users can rate a movie with a rating of one to five stars. Obviously each user wasn't able to rate each movie, so the user movie matrix is sparse. Netflix published an anonymized training and test set. Later researchers found a way to correlate the Netflix data to IMDB data. For privacy reasons, the Netflix data is no longer available. The competition was won in 2008 by a group of teams combining their models. Blending is a form of stacking. The final estimator in blending, however, trains only on a small portion of the train data.

We can arrive at our final answer through majority voting or averaging. It's also possible to assign different weights to each model in the ensemble. For averaging, we can also use the geometric mean or the harmonic mean instead of the arithmetic mean. Usually combining the results of models, which are highly correlated to each other doesn't lead to spectacular improvements. It's better to somehow diversify the models, by using different features or different algorithms. If we find that two models are strongly correlated, we may, for example, decide to remove one of them from the ensemble, and increase the weight of the other model proportionally.