A decision tree is a flowchart-like structure in which each internal node represents a
test on a feature (e.g. whether a coin flip comes up heads or tails) , each leaf node represents a
class label (decision taken after computing all features) and branches represent conjunctions of features that lead to those class labels.
The paths from root to leaf represent
classification rules. Below diagram illustrate the basic flow of decision tree for decision making with labels (Rain(Yes), No Rain(No))
The decision tree Algorithm belongs to the family of supervised machine learning algorithms. It can be used for both a classification problem as well as for regression problem.
The goal of this algorithm is to create a model that predicts the value of a target variable, for which the decision tree uses the tree representation to solve the problem in which the leaf node corresponds to a class label and attributes are represented on the internal node of the tree.
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