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

Discovered on 04 June 10:00 AM CDT.

- 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.

Read full article on
towardsdatascience.com.

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

Discovered on 04 June 09:00 AM CDT.

- 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.

Read full article on
towardsdatascience.com.