A support vector machine allows you to classify data that’s linearly separable.Selecting the appropriate kernel function can be tricky.Selecting, appropriately hyperparameters of the SVM that will allow for sufficient generalization performance.Does not perform well in case of overlapped classes.For larger dataset, it requires a large amount of time to process.SVM is suited for extreme case binary classification.The hyperplane is affected by only the support vectors thus outliers have less impact.Best algorithm when classes are separable.Effective when the number of features are more than training examples.It is really effective in the higher dimension.C: Inverse of the strength of regularization.īehavior: As the value of ‘c’ increases the model gets overfits.Īs the value of ‘c’ decreases the model underfits.īehavior: As the value of ‘ γ ’ increases the model gets overfits.Īs the value of ‘ γ ’ decreases the model underfits.Note: similarity is the angular distance between two points. Using the distance in the original space we calculate the dot product (similarity) of X1 & X2. ||X1 - X2 || = Euclidean distance between X1 & X2 If that projection value is greater than a constant ‘ c’, then it is a positive sample else it is negative. The way that the points are classified as ones that belong to a positive class or negative class is by taking the vector of the point( x), projecting it onto another vector ‘ w’ that is perpendicular to the middle line in the picture(The middle line will be called the median line hence). Let us consider all points in a support vector problem as a vector. the separation between the two class’, the street there is as wide as possible. The entire goal of support vector machines is to maximize the distance between the two dotted lines i.e. The points which are in colors blue and orange are called the support vectors. The ‘ +’ sign is set of points whose classification is ‘- 1’ while the ‘ o’ is for the ‘ +1’ class. In the above picture there are two classes that are differentiated by the shape in which they are depicted. These lines create a margin between the data points. Boundary line situated at positive region is known as Positive Hyperplane and the boundary line situated at negative region is known as Negative Hyperplane. Boundary Line: Two parallel lines drawn to the two sides of Support Vector with the error threshold value, 𝞮(epsilon) are known as the boundary line.In SVR, a hyperplane is a line that is used to predict continuous value. Hyper Plane: In Support Vector Machine, a hyperplane is a line used to separate two data classes in a higher dimension than the actual dimension.There are many types of kernel such as Polynomial Kernel, Gaussian Kernel, Sigmoid Kernel, etc. As SVR performs linear regression in a higher dimension, this function is crucial. Kernel: Kernel is a function that is used to map a lower-dimensional data points into higher dimensional data points.Margins: The distance of the vectors from the hyperplane are called the margins.Support Vector Points:The points closest to the hyperplane are called as the support vector points.SVM is based on the idea of finding a hyperplane that best separates the features into different domains.

It is used for smaller dataset as it takes too long to process. 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). In this article we’re going to study about what is SVM? What exactly is the problem SVM is trying to solve? How do we get the optimal hyperplane? What are the important terminologies? What are the pros and cons of SVM? To fully understand the answers to these questions, we need to go under the hood and explore the mathematics behind SVMs and understand how they work. Support Vector Machine and it’s Mathematical Implementation

Hyperplan vectoriel equation