Support Vector Machine in R

Hits: 58

Support Vector Machine in R

Support Vector Machine (SVM) is a type of supervised machine learning algorithm that can be used for both classification and regression tasks. It works by finding the best boundary, called a hyperplane, that separates different classes or predicts the target variable with the highest accuracy.

In R, there are several packages available for building SVM models, such as the ‘e1071’ and ‘kernlab’ packages. These packages provide functions for creating and training SVM models, as well as functions for evaluating the performance of the model.

The process of building a SVM model in R typically involves the following steps:

  1. Prepare the data: The first step is to prepare the data for the model. This may involve cleaning the data, splitting it into training and testing sets, and scaling the variables.
  2. Define the model: The next step is to define the structure of the model, including the type of kernel (e.g. linear, polynomial, radial) and the regularization parameter.
  3. Train the model: The model is trained using the prepared data. The model will find the best hyperplane that separates different classes or predicts the target variable with the highest accuracy.
  4. Evaluate the model: The model’s performance is evaluated using various metrics such as accuracy, precision, recall, and F1 score.
  5. Make predictions: Once the model is trained and evaluated, it can be used to make predictions on new data.


SVM is a powerful algorithm for both classification and regression problems. It’s particularly useful when you have a large amount of data and complex relationships between variables. It’s also good in dealing with high-dimensional data and can handle non-linear relationships using different types of kernels. However, SVM can be sensitive to the choice of kernel, regularization parameter, and the scale of the data, so it’s important to carefully choose the right parameters and preprocess the data.


In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: Support Vector Machine in R.

Support Vector Machine in R

Personal Career & Learning Guide for Data Analyst, Data Engineer and Data Scientist

Applied Machine Learning & Data Science Projects and Coding Recipes for Beginners

A list of FREE programming examples together with eTutorials & eBooks @ SETScholars

95% Discount on “Projects & Recipes, tutorials, ebooks”

Projects and Coding Recipes, eTutorials and eBooks: The best All-in-One resources for Data Analyst, Data Scientist, Machine Learning Engineer and Software Developer

Topics included: Classification, Clustering, Regression, Forecasting, Algorithms, Data Structures, Data Analytics & Data Science, Deep Learning, Machine Learning, Programming Languages and Software Tools & Packages.
(Discount is valid for limited time only)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.

Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners

There are 2000+ End-to-End Python & R Notebooks are available to build Professional Portfolio as a Data Scientist and/or Machine Learning Specialist. All Notebooks are only $19.95. We would like to request you to have a look at the website for FREE the end-to-end notebooks, and then decide whether you would like to purchase or not.

Please do not waste your valuable time by watching videos, rather use end-to-end (Python and R) recipes from Professional Data Scientists to practice coding, and land the most demandable jobs in the fields of Predictive analytics & AI (Machine Learning and Data Science).

The objective is to guide the developers & analysts to “Learn how to Code” for Applied AI using end-to-end coding solutions, and unlock the world of opportunities!