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What are Support Vector Machines

What are Support Vector Machines? How are They Changing the World

Here’s a quick fact: Non-communicable diseases account for over 74% of all deaths globally each year. While not all risk factors causing these diseases can be easily controlled, early screening and detection can help save millions of lives. This is where Machine Learning (ML) and Support Vector Machines (SVM) come into play. Both of these are being used globally for multiple kinds of research to help in diagnosis and prognosis using numerous ML models. And it’s not just medicine; ML and SVMs are transforming other fields such as speech and text classification, complex computation, liquefaction potential, and more. So if you’re planning to advance your career in ML and are curious about what are support vector machines, this guide will help you.

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What are Support Vector Machines?

Support vector machines are supervised machine learning algorithms that can be used in classification and regression challenges. They are primarily used for classification problems. SVM algorithms help find a hyperplane in an N-dimensional space that distinctly classifies the data points. The dimension of the hyperplane depends on the number of features. For example, if the number of input features is two, then the hyperplane is a line; in the case of three features, the hyperplane becomes a 2D plane, and so on.

Support Vector Machines graphImage Credits: Wikimedia

How Does SVM Work?

Support vector machines sort two data groups by drawing hyperplanes that separate the groups according to the similarities and patterns in data.

Like other supervised learning machines, an SVM requires labeled data to be trained. Training materials for SVMs are classified separately at different points in space and organized into distinctly demarcated groups. With sufficient training, SVMs can also perform unsupervised learning.

Using SVM with Natural Language Classification

SVM is a popular learning algorithm for Natural Language Processing (NLP) tasks such as information extraction, relation extraction, part-of-speech tagging, word sense disambiguation, noun phrase chunking, semantic role labeling, and dependency analysis. Almost all of these applications require the same process:

  1. Transforming the problem into a multiclass classification task
  2. Converting the multiclass problem into several binary classification problems
  3. Training the SVM classifier for each binary classification
  4. Combining the classifiers’ results

What is SVM in Machine Learning

Support Vector Machine Example

Let’s say we want a machine to identify an unknown animal and categorize it either as a cat or a dog. An SVM algorithm will require significant training to recognize the difference. It will start by training the model with various images of cats and dogs to learn about the features of these animals in their various forms. Then it will be tested using a variety of combinations of features so that the SVM creates a decision boundary between two groups of data and builds extreme cases. On the basis of the support vectors, it will be able to classify the unknown animal as a cat or dog.

Support Vector Machine Algorithm Steps

Here are the steps commonly used by SVM algorithms: 

  1. Import the dataset
  2. Explore the data to figure out what they look like
  3. Pre-process the data
  4. Split the data into attributes and labels
  5. Divide the data into training and testing sets
  6. Train the SVM algorithm
  7. Make some predictions
  8. Evaluate the results of the algorithm

Online Course Programming with Python at Carnegie Mellon University School of Computer ScienceHow to Implement SVM in Python and R?

Implementation of SVM in Python

  1. Import the dataset
  2. Split the dataset into training and test samples
  3. Classify the predictors and target
  4. Initialize the Support Vector Machine and fit the training data
  5. Predict the classes for the test set
  6. Attach the predictions to the test set for comparison
  7. Compare the actual classes and predictions
  8. Calculate the accuracy of the predictions
  9. Visualize the classifier
  10. Visualize the predictions

Implementation of SVM in R

  1. Import the data set
  2. Encode the target feature as a factor
  3. Split the dataset into the training set and test set
  4. Feature scale
  5. Fit SVM to the training set
  6. Predict the test set result
  7. Make the Confusion Matrix
  8. Visualize the test set results

Online courses on ML do cover code-level details on how to implement SVM in Python and R.

A Lucrative Career in AI and ML

We hope this guide has helped you understand what are support vector machines as well as how the level of investments in AI and ML has undoubtedly increased in the last five years. Upskill today and become part of the new AI-dominated future. Pick an online course that combines lessons, assessments, hands-on activities, and skill application.

Emeritus offers multiple online courses on predictive analytics that serve key learning outcomes and offer guidance from expert faculty around the world. Explore the extensive AI and ML course catalog by Emeritus to add to your resume.

By Priya S

Write to us at content@emeritus.org

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