Difference between Logistic Regression and Support Vector Machine?

Logistic Regression

It is a statistical technique used to model the relationship between one response variable and one or more explanatory variables. It can be used to predict the probability of an event occurring by fitting data into logistic functions.

Support Vector Machine

It is a supervised machine learning algorithm used for classification and regression analysis. SVM’s are capable of both separating data into two categories, such as spam vs. not spam email messages, or positive vs. negative sentiments in customer feedback.

Logistic RegressionSupport Vector Machine
It is a type of algorithm that helps you to solve classification problems.  It is a model used for both classification and regression.  
It works with already identified independent variable.If it’s text and images, Deep Learning works well. It works best with unstructured data like text and pictures.  
It is vulnerable to overfitting.The risk of overfitting is less in SVM.
Problems to apply logistic regression algorithm.   1. Cancer Detection: It can be used to detect if a patient has cancer (1) or not (0). This is a predictive model that is used to detect if a patient has cancer or not. 2. Marketing: Predict if a customer will purchase a product (1) or not (0). This is a test to see if we can predict the purchase of a product.    Problems that can be solved using SVM   1 – Image Classification   2 – Cancer Detection

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