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Decision Trees

What is a Decision Tree?

When trying to make a critical decision, it’s essential that business leaders examine all of their options carefully. One tool they can make use of to do so is a decision tree. Decision trees are flowchart graphs or diagrams that help inspect all of the decision alternatives and their possible outcomes before coming to a decision.

Each "branch" of the decision tree represents one of the many possible outcomes that are available when making a decision. The branches can be extended when one alternative result leads to another decision that must be made. Each branch has costs added to them that are associated with each choice and the probability each is likely to occur. With this data, leaders can determine the value of each set of branches to identify the best choice.

In its simplest terms, a decision tree is an analysis diagram, which can help decision-makers, when deciding between different options, by predicting possible outcomes, according to the Decision Tree Model blog.  The decision tree provides an overview of the multiple stages that will follow each possible decision.

The Decision Tree Model highlights several advantages to using this technique, including that decision trees are easy to understand and interpret, tiny details that may have been overlooked are taken into consideration and that it helps save time because once the decision is identified, the path to success is easy to follow.

Since there are a lot of calculations involved in creating decision trees, many organizations use dedicated decision tree software to help them with the process. Decision tree tools help organizations draw out their trees, assign value and probabilities to each branch and analyze each possibility.

Tree-based learning techniques are regarded to be one of the best and are mostly used to supervise learning methods. Tree-based procedures empower predictive models with high accuracy, stability and ease of interpretation. They work well at solving any kind of problem at hand.

 

Drawing a decision tree

Decision trees are a helpful technique since they help businesses form a balanced picture of the risks and rewards associated with each possible course of action.

When drawing a decision tree, one should start with drawing a square box, which represents the decision that needs to be made. From that box, each alternative option should be drawn, either downwards or to the right of the box. On each branch, the option should be written. 

Results should then be considered at the end of each branch. If the result of taking the option is unsure, a circle should be drawn, if the result is another decision that needs to be made, a small box should be drawn.

Starting from the new decision squares on the diagram, draw lines representing the options that you could select. From the circles draw lines representing possible outcomes.

Decision-makers should carry on with this process until they have drawn as many of the possible outcomes and decisions they believe can come from the original decision that must be made.

Once the tree is drawn, businesses must analyze it to determine what each branch and outcome represents. Values must be assigned to the possible outcomes as well as the probability of each outcome occurring must be estimated. With this information, they can then calculate which alternative is best.

 

How does a Decision Tree work?

A decision tree is a type of supervised learning algorithm that’s mostly applied in classification problems. It works well for both categorical and continuous input and output variables. In this method, we split the population or sample into two or more homogeneous sets based on the most significant differentiator in input variables.

 

Pruning

The performance of a tree can be further improved by pruning. It involves taking off the branches that have features of low importance. This way, the complexity of the tree is reduced, thus increasing its predictive power by reducing overfitting.

 

Types of Decision Trees

Decision trees can be of two types:

  1. Categorical Variable Decision Tree: Decision Trees that have a categorical target variable are known as categorical variable decision trees. For example- Decisions such as YES or NO.
  1. Continuous Variable Decision Tree: Decision Trees that have a continuous target variable are known as Continuous Variable Decision Trees.

Advantages of Decision Trees:

  1. Easy to Understand: Decision tree outcomes are very simple to interpret even for people from non-analytical backgrounds. It does not need any statistical knowledge to read and interpret them. Its flowchart like graphical representation is very intuitive and users can easily relate their hypothesis.
  1. Useful in Data exploration: Decision trees are one of the quickest ways to identify the most significant variables and the relation between two or more variables. With the help of decision trees, we can create new features that have better power to predict target variables. It can also be used in the data exploration phase. 
  1. Less data cleaning needed: It requires less data cleaning compared to some other modeling methods. It’s not influenced by outliers and missing values to a fair degree.
  2. Data type is not a constraint: Decision trees can handle both numerical and categorical variables. They can also handle multi-output problems.
  1. Non-Parametric technique: Decision trees are considered to be a non-parametric technique which means decision trees have no assumptions about the space distribution and the classifier structure. Tree performance is not affected by non-linear relationships between parameters.

Disadvantages of Decision Trees:

  1. Overfitting: Decision-tree users can create over-complex trees that do not generalize the data well. This is known as overfitting. Overfitting is one of the most practical difficulties for decision tree models. This problem can be solved by setting constraints on model parameters.
  1. Not fit for continuous variables: While working with continuous variables, decision trees lose information, while categorizing variables in different categories. 
  1. Instability: Decision trees can be unstable since tiny variations in data might result in a completely different tree being generated. This is known as variance, which needs to be lowered by methods like bagging and boosting.
  1. Accuracy: Normally, it gives a low prediction accuracy for a dataset as compared to other machine learning algorithms.



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