What takes input as an object described by a set of attributes? A. Tree B. Graph C. Decision graph D. Decision tree

The correct answer is: Decision tree.

A decision tree is a supervised learning model that uses a tree-like structure to make predictions. Each node in the tree represents a test on

To make a prediction, the decision tree is first traversed from the root node to a leaf node, based on the values of the attributes of the input object. The class associated with the leaf node is then returned as the prediction.

Decision trees are a popular machine learning algorithm because they are relatively easy to understand and interpret. They can also be used to solve a variety of problems, including classification, regression, and clustering.

Here is a brief explanation of each option:

• Tree: A tree is a mathematical structure that consists of a set of nodes
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  and edges. The nodes represent objects, and the edges represent relationships between objects. Trees are often used to represent data structures, such as binary trees and search trees.
• Graph: A graph is a mathematical structure that consists of a set of vertices and edges. The vertices represent objects, and the edges represent relationships between objects. Graphs are often used to represent data structures, such as social networks and road networks.
• Decision graph: A decision graph is a graphical representation of a decision tree. Decision graphs are often used to help people understand how decision trees work.
• Decision tree: A decision tree is a supervised learning model that uses a tree-like structure to make predictions. Each node in the tree represents a test on an attribute of the input object, and each branch represents the outcome of the test. The leaves of the tree represent the possible classes of the input object.