What Is the Decision Tree Algorithm All About?

Introduction

A decision tree is a predictive model that helps data scientists to predict the outcome of an event using a specified algorithm or a set of functions. It comes under the classification of supervised learning and comprises root nodes, branch nodes, and leaf nodes. The decision tree follows a hierarchical structure in which an input is evaluated with the help of different functions or a set of algorithms.

Characteristics of a decision tree

When we choose an algorithm in machine learning, it is based on several factors. These factors determine the applications as well as the characteristics of the decision tree.

The first important characteristic of a decision tree is that it segregates and divides data into various categories and operates on this data in a hierarchical manner by a set of functions as well as predefined rules that help in predicting an outcome.

The second important characteristic of a decision tree is that it comes under the supervised learning category. This means that data is labeled and fed into the algorithm in order to train it. In simple terms, we can understand the dependency with the help of an independent variable x as well as a dependent variable y. The independent variable is subjected to different inputs and the consequent variations in the dependent variable are monitored.

The third important characteristic of a decision tree is that it is used in problems where the problem can be solved by regression analysis as well as the classification model. This means that both continuous and discrete data sets can be fed into the decision tree to generate an appropriate outcome and predict results. In the case of continuous datasets, regression analysis is used and in the case of discrete data sets, classification is used. Decision trees can be applied in both cases. In addition to this, a decision tree can also be applied in the case of multivariate analysis.

The most important characteristic of a decision tree is that it is used for quick and short-term analysis instead of long-term analysis. By applying a greedy approach as well as the concept of splitting, the results can be predicted in a short span of time.

Decision tree: advantages and disadvantages

One of the unique advantages of a decision tree is that it is easy and simple to understand. The logic of the decision tree is expressed in the form of graphical techniques like charts that allows all the people involved in a particular project to easily understand the working mechanism of the decision tree. The easy understanding of the decision tree also allows the stakeholders to build trust in the model.

Another important advantage of using a decision tree is that very less effort is required during the operational mechanism of the model. When we compare a decision tree to other models and algorithms, we find that it is the simplest one to use. This is because it does not require complicated and detailed steps related to data cleansing and preparation. This also makes it an easy-to-use and quick deployment model for data scientists.

While working with other models, it is extremely necessary and important to make a lot of assumptions to derive an appropriate result. However, this is not the case in decision trees because the number of assumptions that are required is very less.

In addition to this, the nonlinearity of decision trees as well as their versatility is what makes them suitable for a large number of use cases. This means that the versatility of the decision trees is responsible for increasing their application domain.

The caveat

The most common disadvantage and problem associated with a decision tree is overfitting. There are a lot of variances that are involved while working with a decision tree. The problem of overfitting is common in decision trees and it can lead to results and predictions that are less appropriate.

The training phase of the decision tree is a very time-consuming task and this also limits data scientists from using the model frequently. It is highly likely that the model becomes biased and the predictions may not be as per our expectations.