Support Vector Machines
SVM is a supervised ML algorithm used primarily for classification tasks, however, it can be used for regression problems as well.
What is SVM?
SVM is a classifier that works on the principle of separating hyperplanes. Given a training dataset, the algorithms find a hyperplane that maximizes the separation of the classes and uses these partitions for the prediction of a new dataset. The hyperplane is a subspace of one dimension less than its ambient plane. This means the line is a hyperplane for a two-dimensional dataset.
Where is SVM used?
SVM has similar use cases as that of other classifiers, but SVM is suited well for cases when the number of features/attributes are high compared to the number of data points/records.
By which method can SVM be implemented?
The process to create an SVM model is similar to other classification methods that we studied earlier. The only difference is to import the svm
method from scikit-learn's library. We import the HR
attrition dataset...