Here's another very powerful idea in how people are using machine learning.
You've heard the old saying – “two heads are better than one” – and engineers and scientists are scrambling to apply this to artificial intelligence models.
Ensemble learning is the idea that engineers can compile multiple artificial intelligence entities to study the same problem. In machine learning, ensemble learning will involve various weak and strong learners that each tackle their own aspect of a given challenge.
One example is the use of decision trees – think of a large set of primitive decision classifiers that each gets its own input data and spits out its own outcomes. Some centralized algorithm can take the results from each one of these weak learners and put them all together, and what it comes up with is often a much more precise and fine-tuned result.
Another common example of using ensemble learning is the use of bootstrap aggregation or “bagging.” Bagging can help to reduce excessive variance in a machine learning model, and it can also help with something called overfitting, where the program is unable to really extrapolate results to larger or newer data sets.
With bagging, the model uses a number of weak learners, maybe several dozen, to aggregate a smoother result. Instead of just taking a result from one learner, the model surveys all of the disparate results and tries to put them together to form a bigger picture. Bagging can really enhance the capability of a machine learning project.
Ensemble learning is an interesting and innovative part of machine learning work in general, and something to keep an eye on if you're interested in how scientists are getting more results out of this kind of project.