Support Vector Machines with Python
Introduction Having learnt different optimization and classification methods, you must feel quit confident to start exploring the datasets of interest. However you might very quickly run into a dataset, which no matter how hyper-parameters are set , is just not linearly separable. Surely there is no place for despair, especially since we have just a classifier to deal with these situation, called Support Vector Machine. How it works Support Vector Machine, or SVM, are a set of supervised learning methods used for classification and with a slight change for regression. The core idea of it is to linearly separate the hyper-space of features. The prefix hyper is not occasional, as SVM increases the dimension of feature space to achieve it's goal. The power of the method comes from using kernel functions , which enable it to operate in a high-dimensional, implicit feature space without ever computing the coordinates of the data in that space, but rather by simply computing