![]() ![]() One can read about the two approaches here. Two different examples of this approach are the One-vs-Rest and One-vs-One strategies. Now a classification problem can have only two (binary) classes for separating or can have more than two too which are known as a multi-class classification problems.īut not all classification predictive models support multi-class classification, algorithms such as the Logistic Regression and Support Vector Machines (SVM) were designed for binary classification and do not natively support classification tasks with more than two classes.īut if someone stills want to use the binary classification algorithms for multi-classification problems, one approach which is widely used is to split the multi-class classification datasets into multiple binary classification datasets and then fit a binary classification model on each. We talked about an example of differentiating genders, so such problems are called classification problems. So, to have the least errors in the classification of the data points, that concept will require us to first know the distance between a data point and the separating line. But, while fitting the separating line, we would obviously want such a line that would be able to segregate the data points in the best possible way having the least mistakes/errors of miss-classification. Now that we have seen, how to represent data points, and how to fit a separating line between the points. In D dimensional space, the hyperplane would always be D -1 operator.įor example, for 2-D space, a hyperplane is a straight line (1-D). Here: b = Intercept and bias term of the hyperplane equation The hyperplane equation dividing the points (for classifying) can now easily be written as: (To notice: we have fit a straight/linear line which is 1-D in a 2-D space) Let us look at the equation for a straight line with slope m and intercept c. In this example, the characteristics which will help to differentiate the gender are basically called features in machine learning. Let’s assume we have some data where we(algorithm of SVM) are asked to differentiate between the males and females based on first studying the characteristics of both the genders and then accurately label the unseen data if someone is a male or a female. So, first let’s revise the formulae for how each data is represented in space, and what is an equation of a line which will help in segregating the similar categories, and lastly the distance formula between a data point and the line (a boundary separating the categories). Support Vector Machine basically helps in sorting the data into two or more categories with the help of a boundary to differentiate similar categories. Kindly refer to this article for a complete overview of the working of the algorithm 2. ![]() Since this article is written focusing on the mathematical part. SVM is most commonly used and effective because of the use of the Kernel method which basically helps in solving the non-linearity of the equation in a very easy manner. There is also a subset of SVM called SVR which stands for Support Vector Regression which uses the same principles to solve regression problems. Support Vector Machine is a supervised and linear Machine Learning algorithm most commonly used for solving classification problems and is also referred to as Support Vector Classification. 3.2.3 Use of kernelization to obtain final resultsĪ Support Vector Machine or SVM is a machine learning algorithm that looks at data and sorts it into one of two categories.3.2.1 Final Equation to solve for Imperfect separation.3.2 Case 2: Imperfect Separation dataset.3.1.1 Case 1: Perfect separated binary classified dataset.3.1 Where to use SVM / Background of SVM.Diving Deep into the sea of mathematics.Few Concepts to know before learning the secret behind the algorithm.Gentle Introduction to Support Vector Machine (SVM).In this article, we will learn about the mathematics involved behind the Support Vector Machine for a classification problem, how it classifies the classes and gives a prediction. ![]()
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