Decision Trees and their Importance in Data Mining

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Data Mining refers to the process of looking through vast data sets and extracting the important information and substance about the major point of communication of the data. It is also the process of identifying hidden patterns in a particular data set that requires further division.

A Decision Tree is one of the major data mining tools that makes the process a lot easier. It is compatible with Python programming and works wonders in mining data. One of the advantages of using decision trees in data mining is that they increasingly help in converting raw data into useful and user-readable data.

Read on to gain all the insights about Decision trees in data mining as a tool and how they simplify the whole process.

Decision Trees in Data Mining

Decision trees in data mining are a popular method that creates models for the classification and division of data. The decision tree components include a tree-like structure having nodes, branches, and leaf nodes, hence justifying the name. It is also used as a regression model for making forecasts based on class labels and other attributes aiding the decision-making process.

Advantages of Using Decision Trees in Data Mining

The concept of decision trees in Data Mining comes with the following benefits that showcase its importance in today's world:

Decision Making

It is a very constructive algorithm that simplifies the decision-making process while extracting data. A decision tree can easily choose which data is important and which is irrelevant. It makes the process simple, and redundancy of work can easily be avoided.

Easy Understanding

A decision tree can also be in the form of data visualisation. This makes the process of data mining very easy for coders, as visualised data is easier to understand. Decision trees allow coders to easily fetch raw data from clients and perform the data visualisation algorithm. 


Decision trees are not very expensive. The multiplication of the sub-problem is conducted at every step of the mining process and chooses the relevant node for the extracted data. It automatically chooses the nodes based on logistic regression. Hence, it is a quick and cost-effective method.

Data Categorisation 

Decision trees are capable of drilling with both categorical and numerical data. It can also deal with multiple data sets at the same time. As a result, it solves the problem of multi-class categorisation at the time of mining data.


This method is completely based on a comprehensive analysis of each node and branch and hence the data generated by it can be relied upon. The data can be run through statistical tests to prove its validity. It is also capable of determining accountability and, hence, becomes a reliable method of data mining. 

Little human intervention

Very little human interaction occurs during the preparation of data, which results in a reduction in the amount of time required for cleaning and mining data. Also, unnecessary human interference can create chaos, which this method refrains from doing.

Types of Decision Trees in Data Mining Algorithms:

The most popular decision tree algorithm, known as ID3, was developed by J Ross Quinlan in 1980. The C4.5 algorithm succeeded the ID3 algorithm. Both algorithms used a greedy strategy. 

Here are the most commonly used algorithms for the decision trees in data mining:


When constructing decision trees in data mining, the entire collection of data S is regarded as the root node. The next step is to distinguish data from each set and iterate over every attribute. The algorithm runs through a verification process that adds properties after iteration. However, the ID3 algorithm is an old one, and it consumes a lot of time. It also possesses the disadvantage of overfitting the data.


It is a more developed and sophisticated algorithm that categorises data as samples. In this algorithm, discrete values as well as continuous values, can simultaneously be dealt with. The pruning formula in this algorithm eliminates the irrelevant branches.


This algorithm can handle both classification and logistic regression tasks. The Gini index is an integral part of creating the decision tree. The splitting approach in the cell considerably lowers the cost function. It is one of the best approaches to dealing with regression issues.


CHAID stands for Chi-square Automatic Interaction Detector which is the method that is suitable for working with any kind of variable and attributes. It can be either continuous, ordinal, or nominal variables. It is an advanced algorithm that involves the F-test.


MARS expands to Multivariate Adaptive Regression Splines. It is generally used where the data is present in a non-linear format. It performs regression tasks very well.

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Functions of Decision Tree in Data Mining 


Decision trees are effective instruments for data mining tasks including classification. They use pre-established criteria to categorize individual data points into different groups.


By evaluating input variables and determining the most likely result based on past data patterns, decision trees are able to anticipate outcomes. 


Decision trees provide a visual depiction of the decision-making process, which facilitates users' interpretation along with understanding of the fundamental reasoning. 

Feature Selection

One of the functions of Decision Tree in Data Mining is the ability to determine the most important characteristics or variables that support the categorization or prediction process.


Decision trees offer models that are clear and straightforward, making it possible for users to comprehend the reasoning behind each choice the algorithm makes.

Application of Decision Trees in Data Mining

Information specialists mostly employ decision trees to conduct analytical research. They are also extensively employed in businesses to analyse business challenges. The functions of decision trees in data science are as follows:

Health sector

Decision trees assist in the prediction of diseases and conditions in a patient's health based on parameters like weight, sex, age, etc. Additional forecasts are also made, such as predicting a particular medicine's impact on a patient, keeping in mind its composition and manufacturing history. The health sector is definitely one of the most important functions of decision tree in data mining.

Banking sector

The banking sector uses decision trees to predict a borrower's capacity to repay the loan amount. It helps in determining the eligibility criteria of the bank in advancing loans to the borrowers, considering their financial situation and their repayment ability.

Educational sector

Educational institutions also use decision trees to shortlist students based on their scores and merit lists. It can also help to analyze the payment structure of an institution and how its employees can be paid in a more viable way. Also, listing down the attendance of students can be done with the help of decision trees. This can be considered as one of the most important functions of decision trees in data mining.

Application of decision tree in Data Mining


Decision trees in data mining are used to create models. It is much like an inverted binary tree. The decision tree components comprise nodes, branches, and leaf notes that make it a decision tree. If you are keen to learn about types of decision trees in data mining, then a data science course with placement can be a great choice.

A decision tree can be considered a very effective algorithm that mathematically represents human decisions. Enrol in the Postgraduate Programme In Data Science And Analytics by Imarticus and have a successful career in data science by learning all about the technique of decision trees in data mining.

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