Support Vector Machine (SVM) Machine Learning Models
The decision tree-based models we covered in the last chapter tend to perform well for many problems. However, depending on our problem, other algorithms may work better. One widely used machine learning algorithm is the support vector machine (SVM). Like linear and logistic regression, SVMs have been around for a while – since 1963. SVMs can be used for regression and classification, sometimes called support vector regressors (SVRs) and support vector classifiers (SVCs). Although SVMs have been around for a while and have become less popular with the rise of other ML algorithms, it's still worth trying SVMs as one of your ML algorithms for supervised learning problems. The basic theory and usage of SVMs will be the focus of this chapter. Specifically, we'll cover:
- The basic idea behind SVMs
- How to use SVMs for classification and regression with
sklearnandpycaret - How to tune SVM hyperparameters...
