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.
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.
A 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:
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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