Introduction:

The support vector machines algorithm is a supervised machine learning algorithm that can be used for both classification and regression. In this article, we will be discussing certain parameters concerning the support vector machines and try to understand this algorithm in detail.

Geometrical Interpretation:

For understanding, let us consider the SVM used for classification. The following figure shows the geometrical representation of the SVM classification.

After taking a look at the above diagram you might notice that the SVM classifies the data a bit differently as compared to the other algorithms. Let us understand this figure in detail. The Red-colored line is called as the ‘hyperplane’. This is basically the line or the plane which linearly separates the data. Along with the hyperplane, two planes that are parallel to the hyperplane are created. While creating these two planes, we make sure that they pass through the points that are closest to the hyperplane. These points can be called as the nearest points. The hyperplane is adjusted in such a way that it lies exactly in the middle of the two parallel planes. The distance between these two planes is called the ‘margin’. The advantage of these two parallel planes is that it helps us to classify the two classes in a better way. Now a question arises that there can be multiple hyperplanes and out of them why did we select the one in the above diagram? The answer to that is we select the hyperplane for which the margin i.e the distance between the two parallel planes is maximum. The points that are on these two parallel planes are called support vectors. The above figure is obtained after training the data. Now for the classification of unknown data or the testing data, the algorithm will only take into consideration the reference of the support vectors for classification.

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