SVM Hyperparameters Explained with Visualizations

SVM Hyperparameters Explained with Visualizations

In this post, we dive deep into two important hyperparameters of SVMs, C and gamma, and explain their effects with visualizations. So I will assume you have a basic understanding of the algorithm and focus on these hyperparameters.

Support Vector Machine (SVM) is a widely-used supervised machine learning algorithm. It is mostly used in classification tasks but suitable for regression tasks as well.

In this post, we dive deep into two important hyperparameters of SVMs, *C *and *gamma, *and explain their effects with visualizations. So I will assume you have a basic understanding of the algorithm and focus on these hyperparameters.

SVM separates data points that belong to different classes with a decision boundary. When determining the decision boundary, a soft margin SVM (soft margin means allowing some data points to be misclassified) tries to solve an optimization problem with the following goals:

  • Increase the distance of decision boundary to classes (or support vectors)
  • Maximize the number of points that are correctly classified in the training set

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