# Articles related to "margin"

## Support Vector Machines (SVM) clearly explained: A python tutorial for classification problems with 3D plots

• In this article, I am not going to go through every step of the algorithm (due to the numerous amount of online resources) but instead, I am going to explain the most important concepts and terms around SVMs. The SVCs aim to find the best hyperplane (also called decision boundary) that best separates (splits) a dataset into two classes/groups (binary classification problem).
• To get the main idea think the following: Each observation (or sample/data-point) is plotted in an N-dimensional space with Nbeing the number of features/variables in our dataset.
• The Support vectors are just the samples (data-points) that are located nearest to the separating hyperplane.
• The distance between the hyperplane and the nearest data points (samples) is known as the SVM margin.
• The kernel-SVM computes the decision boundary in terms of similarity measures in a high-dimensional feature space without actually doing the projection.

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## Support Vector Machines (SVM) clearly explained: A python tutorial for classification problems with 3D plots

• In this article, I am not going to go through every step of the algorithm (due to the numerous amount of online resources) but instead, I am going to explain the most important concepts and terms around SVMs. The SVCs aim to find the best hyperplane (also called decision boundary) that best separates (splits) a dataset into two classes/groups (binary classification problem).
• To get the main idea think the following: Each observation (or sample/data-point) is plotted in an N-dimensional space with Nbeing the number of features/variables in our dataset.
• The Support vectors are just the samples (data-points) that are located nearest to the separating hyperplane.
• The distance between the hyperplane and the nearest data points (samples) is known as the SVM margin.
• The kernel-SVM computes the decision boundary in terms of similarity measures in a high-dimensional feature space without actually doing the projection.

save | comments | report | share on