It can solve linear and non-linear problems and work well for many practical problems. In this set, we will be focusing on SVC. For example, in two-dimensional space a hyperplane is a straight line, and in… Support Vector Machines are a very powerful machine learning model. The street becomes dashed and solid lines. The idea of SVM is simple: The algorithm creates a line or a hyperplane which separates the data into classes. Support vector machines attempt to pass a linearly separable hyperplane through a dataset in order to classify the data into two groups. Red dots stand for objects on the left side of the street; green dots stand for those on the right. This hyperplane is a linear separator for any dimension; it could be a line (2D), plane (3D), and hyperplane (4D+). Whereas we focused our attention mainly on SVMs for binary classification, we can extend their use to multiclass scenarios by using techniques such as one-vs-one or one-vs-all, which would involve the creation of one SVM for each pair of classes. Check out this graph from An Introduction to Statistical Learning : 2. SVM or Support Vector Machine is a linear model for classification and regression problems. What is Support Vector Machine (Classifier) Back to the ML world, those cars, building, pedestrians all become dots now. 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. What is hyperplane: If we have p-dimensional space, a hyperplane is a flat subspace with dimension p-1.

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