Decision Trees and their Importance in Data Mining

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. It increasingly helps in converting raw data into useful and user-readable data.

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

Decision Tree in Data Mining

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

Importance of Decision Tree in Data Mining

The concept of decision tree in Data Mining comes with the following advantages 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. 

Cost effectiveness

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 at the same time. As a result, it solves the problem of multi-class categorisation at the time of mining data.

Reliability 

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 the validation. It is also capable of determining accountability and hence becomes a reliable method of data mining. 

Little human intervention

Very little human interaction occurs at the time of 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.

Algorithm of Decision Tree in Data Mining

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 used algorithms of the decision tree in data mining:

ID3

When constructing a decision tree 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.

C4.5

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.

CART

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

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

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

Application of Decision Tree in Data Mining

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

Health sector

Decision tree essays 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 trees 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.

Conclusion

Decision tree in data mining is used to create models. It is much like an inverted binary tree. It is constituted of nodes, branches, and leaf notes that make it a decision tree. If you are keen to learn about decision trees and 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 for 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.