Use scikit-learn to Support Vector Machine to classify;
In this notebook, we will use SVM (Support Vector Machines) to build and train a model using human cell records, and classify cells to whether the samples are benign or malignant.
SVM works by mapping data to a high-dimensional feature space so that data points can be categorized, even when the data are not otherwise linearly separable. A separator between the categories is found, then the data is transformed in such a way that the separator could be drawn as a hyperplane. Following this, characteristics of new data can be used to predict the group to which a new record should belong.
The example is based on a dataset that is publicly available from the UCI Machine Learning Repository (Asuncion and Newman, 2007)