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.