In the shape of a tree structure, a decision tree constructs regression or classification models. It incrementally cuts down a dataset into smaller and smaller sections while also developing an associated decision tree. A tree with decision nodes and leaf nodes is the end result.
One of the most widely used and useful models for supervised learning is the Decision Tree. It can be used to tackle both regression and classification problems, albeit the latter is more widely utilised. There are three sorts of nodes in this tree-structured classifier.
The key distinction between the random forest method and the decision tree algorithm is that decision trees are graphs that depict all possible outcomes of a decision using a branching strategy. The random forest algorithm, on the other hand, produces a set of decision trees that work in accordance with the output.
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