Describe what a Support Vector Machine is.
Understand the Problem
The question asks for a description of Support Vector Machines (SVMs). This involves explaining what they are, their purpose, and potentially some key characteristics or advantages.
Answer
A Support Vector Machine (SVM) is a supervised machine learning algorithm used for classification and regression tasks.
A Support Vector Machine (SVM) is a supervised machine learning algorithm that is used for classification and regression tasks. SVMs are especially useful for classifying data sets into two groups.
Answer for screen readers
A Support Vector Machine (SVM) is a supervised machine learning algorithm that is used for classification and regression tasks. SVMs are especially useful for classifying data sets into two groups.
More Information
SVMs are known as max-margin classifiers, this means that SVM algorithms search for the hyperplane that maximizes the margin between the two classes.
Tips
It can be difficult to select the appropriate kernel function for a given problem, which can affect the accuracy of the SVM model.
Sources
- support vector machines (SVMs) - IBM - ibm.com
- Support Vector Machine (SVM) Algorithm - Analytics Vidhya - analyticsvidhya.com
- 1.4. Support Vector Machines - Scikit-learn - scikit-learn.org
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