# Understanding Decision Tree Classifier

conceptually with example using ID3 algorithm. In this post, we are going to discuss the workings of Decision Tree classifier conceptually so that it can later be applied to a real world dataset.

In this post, we are going to discuss the workings of Decision Tree classifier conceptually so that it can later be applied to a real world dataset.

Classification can be defined as the task of learning a target function fthat maps each attribute set xto one of the predefined labels y.

Examples:

• Assigning a piece of news to one of the predefined categories.
• Detecting spam email messages based upon the message header and content
• Categorising cells as malignant or benign based upon the results of MRI scans
• Classifying galaxies based upon their shape

Decision Tree can be a powerful tool in your arsenal as Data Scientist or a Machine Learning Engineer when working with real world datasets. Decision Trees are also used in tandem when you are building a Random Forest classifier which is a culmination of multiple Decision Trees working together to classify a record based on majority vote.

Decision Tree is constructed by asking a serious of questions with respect to a record of the dataset we have got. Each time an answer is received, a follow-up question is asked until a conclusion about the class label of the record. The series of questions and their possible answers can be organised in the form of a decision tree, which is a hierarchical structure consisting of nodes and directed edges. A tree has three types of nodes:

• root node that has no incoming edges and zero or more outgoing edges.
• Internal nodes, each of which has exactly one incoming edge and two or more outgoing edges.
• Leaf or terminal nodes, each of which has exactly one incoming edge and no outgoing edges.

In a decision tree, each leaf node is assigned a class label. The non-terminal nodes, which include the root and other internal nodes, contain attribute test conditions to separate records that have different characteristics.

Let us construct a Decision Tree intuitively given a dataset before diving into the mathematics of it.

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