A common task in Machine Learning is to classify data. Given a data point cloud, sometimes linear classification is impossible. In those cases we can use a Support Vector Machine instead, but an SVM can also work with linear separation.
We loading the Iris data, which we’ll later use to classify. This set has many features, but we’ll use only the first two features:
- sepal length
- sepal width
Support Vector Machine Example
Separating two point clouds is easy with a linear line, but what if they cannot be separated by a linear line?
In that case we can use a kernel, a kernel is a function that a domain-expert provides to a machine learning algorithm (a kernel is not limited to an svm).
The example below shows SVM decision surface using 4 different kernels, of which two are linear kernels.