# Support Vector Machine vs Logistic Regression

Support Vector Machine vs Logistic RegressionGeorgios DrakosBlockedUnblockFollowFollowingAug 12Support Vector Machine (SVM) is an algorithm used for classification problems similar to Logistic Regression (LR)..How you define this notion of “best” gives you different models like SVM and logistic regression (LR).What is Support Vector Machine?The objective of the support vector machine algorithm is to find the hyperplane that has the maximum margin in an N-dimensional space(N — the number of features) that distinctly classifies the data points.Data points falling on either side of the hyperplane can be attributed to different classes..These are the points that help us build our SVM.What is Logistic Regression?In logistic regression, we take the output of the linear function and squash the value within the range of [0,1] using the sigmoid function( logistic function)..This justifies the name ‘logistic regression’.Note that the difference between logistic and linear regression is that Logistic regression gives you a discrete outcome but linear regression gives a continuous outcome.Steps that logistic regression goes through to give you your desired outputSigmoid Activation FunctionWe want to maximize the likelihood that a random data point gets classified correctly, which is called Maximum Likelihood Estimation..If logistic regression fails and you have reason to believe your data won’t be linearly separable, try an SVM with a non-linear kernel like a Radial Basis Function (RBF).Illustrates where to use each method SVM and Logistic RegressionTake home messageSVM tries to find the widest possible separating margin, while Logistic Regression optimizes the log likelihood function, with probabilities modeled by the sigmoid function.SVM extends by using kernel tricks, transforming datasets into rich features space, so that complex problems can be still dealt with in the same “linear” fashion in the lifted hyper space.Thanks for reading and I am looking forward to hear your questions :)Stay tuned and Happy Machine Learning.P.S If you want to learn more of the world of machine learning you can also follow me on Instagram, email me directly or find me on linkedin.. More details