Internet of Things for Architects

Random forest

A random forest is a subset of another machine learning model called the decision tree. A decision tree, as the diagram at the start of this section shows, is a group of learning algorithms that are part of the statistical set. A decision tree simply takes several variables into consideration and produces a single output that classifies the set. Each element evaluated is called the set. The decision tree produces a set of probabilities that a path has taken based on the input. One form of a decision tree is the Classification and Regression Test (CART), developed by Breiman in 1983. 

We now introduce the notion of bootstrap aggregating or bagging. When you have a single decision tree being trained, it is susceptible to noise ...