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In machine learning, decision trees are used to structure the algorithm. To separate dataset features using a cost function, a decision tree technique will be employed. Before being optimized, the decision tree is pruned to remove branches that may employ extraneous features.

A decision tree is a form of probability tree that allows you to make a decision on a given process. For example, you might want to decide whether to manufacture item A or item B, or whether to invest in option 1, option 2, or option 3.

A decision tree is a type of supervised machine learning that is used to categorize or forecast depending on the answers to a previous set of questions. The model is supervised learning in the sense that it is trained and tested on data that contains the desired categorisation.

  • Simple to comprehend and interpret.
  • Little data preparation is required.
  • The cost of using the tree (predicting data) is proportional to the quantity of data points needed to train the tree.
  • Capable of dealing with both numerical and category data.
  • Capable of dealing with multi-output difficulties.

Decision trees are a popular method in machine learning and are often used in operations research, notably in decision analysis, to help determine the strategy most likely to achieve a goal.

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