If you’re into a data-driven industry, you already know how tedious classification and prediction process is. It can waste all your productivity if you do not know some of the classic advanced algorithms. The result will be less accurate, and the process will be tiring processing the massive database.
Managing single-handedly is never an easy job, and there are high chances that you will miss out on many while doing the analysis. Yes, it’s that tough, to be honest, but there are other ways you can get the best result in no time with high accuracy. And one such robust algorithm is decision trees.
In this blog, we are going to learn about five top-notch applications of decision trees. But before hitting straight into it, let’s know a bit more about decision trees and their impact on trending technologies.
Decision trees are the dynamic and popular tools for classification and prediction models. It’s a flowchart-like structure containing leaf and nodes, where the internal nodes denote the test in an attribute. And each node represents an outcome of the trial, and each leaf node (terminal node) holds a class label.
Construction and Working Process of Decision Trees
The Decision Trees Algorithm is a self-learning algorithm that split the source set into subsets based on the attribute value tests. This process is on loop for each derived subset recursively. The process is complete when the node’s subset has the same value as that of the target variable or the splitting techniques don’t have any value to the prediction. The construction of a decision tree is relatively easy, it doesn’t require any domain knowledge, and it can handle high-dimensional data with higher accuracy.
Decision Trees classify the whole data into trees from the root to the leaf node to organize the instances. It starts from classifying the tree’s root nodes, then testing the attributes based on nodes, then moving down into the corresponding value. This process continued for the subtree rooted at the new node.
Highs and Lows of Decision Trees
Highs
Decision Trees generate understandable rules, can perform classification without much computation while handling both variables ( continuous and categorical).
It provides a clear indication about the field that is crucial for classification and predictions.
Lows
Decision Trees give low accuracy results for predicting the value of continuous variables. They are prone to errors in classification problems having many classes and relatively small training examples.
Decision Trees are expensive to train. You must sort them to find the best split. In other algorithms, you can use various fields based on their weights, which is again expensive.
3 Applications of Decision Trees in Classifying and Predicting Models
Customer Relationship Management
If you are already running a business, you understood by now what it takes to run a business successfully. Having varieties of products and services is never a big deal unless you know your customers are in their shoes. When you understand the pain points, you connect with them well, and ultimately it increases customers’ trust, driving more revenues and increasing more sales.
Decision Trees helps in finding out:
Users who rarely shop online
Users who often shop online
When you measure these two kinds of customers, you get to know how much time they spend on websites, their search queries, or their interest in buying. How frequently they search for that particular item, in other words, the demand and urgency of that specific product. When you have this data, it’s easier for you to make decisions and retain the customer for a longer time.
The application of decision trees specifies the customers’ needs and preferences and the success of online shopping.
Fraudulent Statement Detection
Frauds are everywhere, and fraudsters are looking for customers to get them in their nets all the time, in multiple ways. It comes under the Fraudulent Financial Statement, shortly FFS, where the prime purpose is to reduce government tax using statistical methods.
It is sturdy to know all the hidden data and assumptions, so bodies like FFS make your work easy for you by predefining the relationships among the multiple variables in the financial statement.
According to recent research, the use of decision trees and models to identify and detect FFS. This model can catch all the non-fraud cases and classify 92% of fraud cases using FFS with high accuracy.
Fault Diagnosis
Detecting faults is a humongous task; how precisely you do this process somewhere, you leave without spotting the flaw or errors. It is because algorithms are complex. You need to know the purpose of using them to make the best use of them. Otherwise, they will produce faulty errors, which will again be a headache if not diagnosed at the earliest stage.
Decision trees’ particular use is to remove insignificant attributes using KNN algorithms within the dataset is one of the best contributions of decision trees.
Final Words
You get all the basics you want to know about decision trees. You get to know what decision trees are, the construction, and —however, decision trees’ working process, decision trees’ role on classification and prediction processes. And the positives and negatives of decision trees. Some of the classic examples are maintaining seamless customer relationship management, fraudulent statement detection, and fault diagnosis mechanism.
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