Types of Decision Models - Group 4 - 2018/2019, Semester B, Quartile 3: Difference between revisions
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= Nearest Neighbour Strategy = | = Nearest Neighbour Strategy = | ||
NearestNeighbour, short NN, is a mathematical decision model. It is a machine learning decision model, in the sense that existing solutions, often denoted as training data, are used for NN to be able to accurately make predictions about new data such as a user which wants a solution for their airport. This decision model can make the choice which solution fits best to the user. Nearest Neighbour is based on the machine learning strategy KNearestNeighbors </ref "Brilliant">[https://brilliant.org/wiki/k-nearest-neighbors/ | NearestNeighbour, short NN, is a mathematical decision model. It is a machine learning decision model, in the sense that existing solutions, often denoted as training data, are used for NN to be able to accurately make predictions about new data such as a user which wants a solution for their airport. This decision model can make the choice which solution fits best to the user. Nearest Neighbour is based on the machine learning strategy KNearestNeighbors </ref name = "Brilliant">[https://brilliant.org/wiki/k-nearest-neighbors/ "Brilliant.org"], Retrieved 17 March 2019</ref>. | ||
== Picking variables / attributes == | == Picking variables / attributes == | ||
In order for Nearest Neighbour to work | In order for Nearest Neighbour to work | ||
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Introduction
In this section, we will investigate some different approaches for decision models.
Nearest Neighbour Strategy
NearestNeighbour, short NN, is a mathematical decision model. It is a machine learning decision model, in the sense that existing solutions, often denoted as training data, are used for NN to be able to accurately make predictions about new data such as a user which wants a solution for their airport. This decision model can make the choice which solution fits best to the user. Nearest Neighbour is based on the machine learning strategy KNearestNeighbors </ref name = "Brilliant">"Brilliant.org", Retrieved 17 March 2019</ref>.
Picking variables / attributes
In order for Nearest Neighbour to work