The Difference Between SVM and Decision Trees

Support vector machines (SVMs) and decision trees are both machine learning models that can classify data into distinct categories. Both techniques have advantages and disadvantages, and you can decide which model is best for your needs based on your project and how you want to use it. Explore support vector machines and decision trees, including how they work, applications you can use them for, and the pros and cons of each AI model. 

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What is a support vector machine?

An SVM is a type of machine learning model used for binary classification, that is, sorting data into two distinct groups. This algorithm plots data points from the input and then finds the optimal position for a line or hyperplane between two groups. The SVM finds this hyperplane in training and can then compare new items against its training to predict which category to classify the new data. 

For example, if you wanted to sort images with black backgrounds from images with white backgrounds, you would first train your SVM with both kinds of images. When the SVM is properly trained, it will understand the characteristics of each class and accurately predict which class it should sort new data into. 

What is SVM used for? 

SVMs primarily perform binary classification, but scientists have adapted methods of using SVMs for multiclass classification. A few specific examples of how you can use an SVM in different industries include: 

• Protein and cancer classification: Scientists can use SVMs to analyze protein sequences or the genomic features of cancer for fast and accurate classification. These algorithms can help predict diagnoses, suggest the best medications for patients, and provide insight into the biological process of tumors. 

• Speech, image, and text recognition: SVMs can support speech recognition, image recognition, and text recognition in combination with other models or by using multiple SVMs within a larger AI model. For example, a network of SVMs all trained to recognize different words could work together to understand text. Or, you could use an SVM paired with a convolutional neural network to quickly sort data and recognize images. 

• Geographical information systems: You can use geographic information systems and an SVM to analyze terrain above and below ground. You could use the data to understand the risks or map the aftermath of natural disasters such as earthquakes or landslides.

Advantages and challenges of SVMs

SVMs offer many advantages and you may prefer them in some cases, although they do have limitations you should be aware of. Some of the advantages of SVMs include their ability to operate in high-dimension spaces and their versatility: 

• High-dimensional spaces: You can use SVMs to go beyond two or three dimensions into more complex models. 

• Versatile: You can use different functions called kernels to give the SVM instructions about how to make decisions, allowing you to modify your SVM for different applications. 

SVMs also have limitations, including long and laborious training time, due to the amount of training data needed and the complexity of the algorithm. Another challenge is developing the appropriate classes for multiclass problems. 

What are decision trees?

A decision tree is a type of machine learning algorithm that uses a flowchart-like series of decisions to make predictions about input data. You may be familiar with the game “20 Questions,” where you think of a person, place, or thing, and another player asks up to 20 questions about it, narrowing down the possibilities until they can accurately guess the mystery. A decision tree functions in a similar way, making a sequence of decisions about input data by looking at its features. 

In training, a decision tree can optimize each decision point or node to find the best way to split the data beyond that point. This helps a decision tree become more accurate in its predictions. The random forest is another type of machine learning algorithm, which uses many decision trees in one model (resembling a forest of trees). With a random forest algorithm, each decision tree makes a prediction about the input data, and then they collectively vote on the answer. The answer that the majority of the trees found to be the likely correct answer is given as the output. 

What is a decision tree used for? 

You can use a decision tree for classification and regression, a technique for understanding which variables impact the outcome and for making predictions. Specific examples of how you might use a decision tree in different industries include customer segmentation, fraud detection, risk assessment, health care, and agriculture. 

• Customer segmentation: You can use a decision tree to sort your customer base into different target markets, such as by demographics (e.g., age, gender, and location) or by other traits (e.g., their interests). You can use this information to create personalized marketing campaigns that speak to your customers’ unique pain points. 

• Fraud detection: Decision trees and random forest models can analyze credit card transactions to predict which might be fraudulent. 

• Risk assessment: You can use decision trees to make predictions about the creditworthiness of potential borrowers, which is an important task for banks and other lending organizations. 

• Predicting patient outcomes: Health care professionals can use a decision tree to look at potential treatments and predict their outcomes to help make a more informed decision about the best treatments and medications to use. 

• Agriculture: You can use a decision tree to make predictions about variables that impact agriculture, such as weather, disease, and performance of crops. 

Advantages and disadvantages of decision trees

While decision trees offer many benefits, it’s important to consider their limitations as well. Some of the advantages of a decision tree include: 

• Easy to understand: You can chart and visualize a decision tree, and many people are familiar with the analog version of how they work in one form or another outside of machine learning. 

• Flexible with little data preparation: A decision tree can save you time because it does not require data normalization. You can use data with different value types and, in some cases, missing data. 

• Used for classification and regression: Another way that decision trees are flexible is because you can use them in more scenarios and for more specific applications. 

Yet, you should also be aware of the limitations and challenges that can sometimes come with decision trees. They can be prone to overfitting, for example, and they can create models that are far too complex to generalize to new data. Another challenge is that small variations in your data can lead to the model creating entirely different decision trees, which can make this type of model a little unstable. Using a random forest, also known as an ensemble of decision trees, can help you reduce these challenges. 

The difference between SVM and decision trees

The difference between SVMs and decision trees is in how they approach the problem of classifying data into distinct categories. SVMs find the categories mathematically by plotting the characteristics of the data and finding the appropriate hyperplane that splits the data into classes. A decision tree, on the other hand, makes a series of decisions about the features of data to predict which class to assign the data. 

Getting started with Coursera

SVMs and decision trees are both machine learning models that classify data. If you want to learn more about SVMs, decision trees, or machine learning, consider a course or Guided Project on Coursera. You could enroll in a program such as Build Decision Trees, SVMs, and Artificial Neural Networks offered by CertNexus as part of its CertNexus Certified Artificial Intelligence Practitioner Professional Certificate. Or you could gain hands-on experience with a Guided Project such as Decision Tree Classifier for Beginners in R. 

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