Question

In a Decision Tree, Predictor variables are represented by​

Answer 1

Answer:

A Decision Tree is a Supervised Machine Learning algorithm which looks like an inverted tree, wherein each node represents a predictor variable (feature), the link between the nodes represents a Decision and each leaf node represents an outcome (response variable).

Answer 2

In a Decision Tree, each node represents a predictor variable.

• A decision tree is a logical model that shows how the value of a target variable can be predicted using the values of a set of predictor variables. It is represented as a binary (two-way split) tree. The following is an example of a decision tree: The rectangular boxes in the tree are referred to as "nodes."
• Variables that are used to predict another variable or outcome are known as predictor variables. Predictor variables, unlike independent variables, are not usually manipulated by researchers, do not imply that one variable affects another, and are utilised in nonexperimental designs.
• It is also known as the response variable in statistics. In the context of machine learning, predictor variables are the input data or variables that are translated to the target variable by an empirical relationship that is usually found through the data. They're known as predictors in statistics.
• Chance nodes, decision nodes, and end nodes are the three sorts of nodes. The probability of specific outcomes is represented by a chance node, which is represented by a circle.

Hence, In a Decision Tree, each node represents a predictor variable.

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