Decision Tree

               1. How does Decision Tree Works?

               2. Types of Decision Trees

                    1. Categorical Variable Decision Tree

                    2. Continuous Variable Decision Tree

               3. Assumptions while creating Decision Tree

               4. Advantages of Decision Tree

               5. Disadvantages of Decision Tree

               6. Examples of Decision trees

          Previously from writing long paragraphs and explanations of a particular topic or decision to make a person understand, we now have moved forward with the concept of decision tree which is a simple way to visualize a decision.

A decision tree is a flowchart-like structure in which each internal node represents a “test” on an attribute. A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility, each branch represents the outcome of the test, and each leaf node represents a class label. The paths from the root to leaf represent classification rules. Tree-based learning is proved to be simpler as it gives a clear idea about the decision. They are commonly used as it gives more accuracy. It has influenced a wide area of machine learning, covering both classification and regression.

          As the name suggests tree, this  flowchart is drawn upside down having its root at the top. Some of the terms used for decision tree are root node, splitting, decision node, leaf/terminal node, branch/sub-tree, parent & child node. Decision trees have a natural “if … then … else …” construction that makes it fit easily into a programmatic structure. 

One of the example is showed in the image

A decision tree is often a generalization of the experts’ experience, a means of sharing knowledge of a particular process. For example, when there was no scalable machine learning algorithms, the credit scoring task in the banking sector was solved by experts. The decision to grant a loan was made on the basis of some empirically derived rules that could be represented as a decision tree.

They likewise are appropriate to order issues where characteristics or highlights are deliberately checked to decide a last classification. For instance, a choice tree could be utilized adequately to decide the types of a creature.

       Choice tree is a sort of regulated learning calculation (having a pre-characterized target variable) that is for the most part utilized in characterization issues. It works for both straight out and persistent info and yield factors. In this method, we split the populace or test into at least two homogeneous sets (or sub-populaces) in view of most critical splitter/differentiator in input factors.

           Decision tree which has a categorical target variable then it called a categorical variable decision tree. E.g.:- In the above scenario of student problem, where the target variable was “Student will play cricket or not” i.e. YES or NO.

            Decision tree has continuous target variable then it is called as Continuous Variable Decision Tree.

One more general example of a decision tree is shown in the image below:

As a result, the decision making tree is one of the more popular classification algorithms being used not only in machine learning but in day to day life as well. Because of their simplicity, tree diagrams have been used in a broad range of industries and disciplines including civil planning, energy, financial, engineering, healthcare, pharmaceutical, education, law, and business.

I hope you enjoyed reading this article and finally, you came to know about the Decision Tree algorithm. 

   

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