Classification Trees


Classification Trees analysis is a subcategory of Supervised Learning where the Predictor variable is the Classification to which the data belongs.

The simplest Classification Trees is a Binary Classification Trees as in:

  • Cat
  • NOT Cat

Classification and Classification Trees is a subcategory of Supervised Learning where the goal is to predict the Classification labels of new instances, based on past observations. Those Classification labels are discrete, unordered values that can be understood as the group memberships of the instances.

The set of Classification labels does not have to be of a binary nature. The Mapping function learned by a Supervised Learning algorithm can assign any Classification label that was presented in the training dataset to a new, unlabeled instance.

A typical example of a multiclass Classification task is handwritten character recognition. Where the Training dataset that consists of multiple handwritten examples of each letter in the alphabet. However, our Machine Learning model would be unable to correctly recognize any of the digits zero to nine, for example, if they were not part of our training dataset.

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