Please explain Support Vector Machines (SVM) like I am a 5 year old. • r/MachineLearning

This is tough for five-year-olds, but I'll give it a shot for ten-year-olds. Like a lot of other machine learning algorithms, SVMs take some data to start with that's already classified (the training set), and tries to predict a set of unclassified data (the testing set). The data that we have often has a lot of different features, and so we can end up plotting each data item as a point in space, with the value of each feature being the value at a particular coordinate. Now (for two data features) what we want to do is find some line that splits the data between the two differently classified groups of data as well as we can. This will be the line such that the distances from the closest point in each of the two groups will be farthest away.