Throughout this chapter you are going to see the following terms used. Here is the context for what each of them means:

• Learner: This refers to a machine learning algorithm.
• Model: This refers to a machine learning model.
• Hyper-parameters: These are the parameters used to adjust and regulate (hopefully) the machine learning model.
• Targets: These are more commonly referred to as a dependent variable. In most notations, this will be y. These are the values that we are attempting to model.
• Observations: These are the feature matrix, which contains all the information we currently have about the targets. In most notations, this will be x.

Throughout most of our examples we will be focusing on two namespaces within SharpLearning. They are:

• SharpLearning.DecisionTrees
• SharpLearning.RandomForest

With that behind us, let’s start digging into SharpLearning and show you a few concepts relative to how it works.