Classification in R – SVM in R

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Classification in R – SVM in R

Support Vector Machine (SVM) is a popular method for classification in machine learning and data analysis. It’s a type of algorithm that can sort items into different categories based on their characteristics.

An SVM works by finding the best boundary, or “decision boundary,” that separates the different classes in a dataset. This boundary is chosen to maximize the distance between itself and the closest data points from each class, known as “support vectors.” By maximizing this distance, an SVM can create a boundary that is less likely to be affected by noise or outliers in the data.

In R, the “e1071” package provides an implementation of SVM. This package contains functions that can be used to train and evaluate an SVM model, as well as tools for visualizing the results.

One advantage of using SVM in R is that it can handle complex decision boundaries and work well with high dimensional data. It also has a good performance even with a small sample size.

However, SVM can have a disadvantage when dealing with big datasets and large number of features because of its computational complexity. Also, it is sensitive to the choice of parameters.

Overall, SVM in R is a powerful method for classification. It can help you accurately sort your data into different categories even when the decision boundary is complex and the data is high dimensional. However, it’s important to keep in mind that it can be computationally expensive and sensitive to parameter choice.


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: Classification in R – SVM in R.

Classification in R – SVM in R

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