Support Vector Machines — All you need to know

Support Vector Machines — All you need to know

Support vector machines so called as SVM is a supervised learning algorithm which can be used for classification and regression problems as support vector classification (SVC) and support vector regression (SVR). It is used for smaller dataset as it takes too long to process.

How does it work?

SVMs are a very powerful algorithm for classification and regression tasks; they not only aim to classify the data but also aim to find the best possible boundary, namely, the one that maintains the largest distance from the data points.

In SVMs we determine the optimal lie to be one that properly separates the data, but also has the greatest distance between the line and the closest point, maximizing the margin.

How is Error considered in SVM?

The idea is to punish the points that are incorrectly classified, eve in the margin, the further an incorrect point is from the decision line, the greater the error. This means that even if a point is on the correct side of the decision boundary, but in the margin, it will be punished nonetheless.

In SVMs we want as large a margin as possible while maintaining the accuracy of course. We give smaller margins greater error or punishment because we want to incentive a line that fits the data best, meaning it also has a lot of room between the decision boundary and the margin.

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