We have covered Decision tree models for solving classification and regression problems in the previous chapter. In this chapter, we will cover two important models of supervised and unsupervised learning techniques which are the Nearest Neighbors method, which uses the instance-based learning model, and the Support Vector Machines (SVM) model, which uses kernel methods based learning model. For both methods, we will learn the basics of the technique and see how it can be implemented in Apache Mahout, R, Julia, Apache Spark, and Python. The following figure depicts different learning models covered in this book and the techniques highlighted will be dealt in covered in this chapter.
The following topics are covered in-depth in this chapter:
Instance-based learning models
Introduction to instance-based learning
Lazy and eager learning
A brief look at different algorithms/approaches of instance-based learning techniques Nearest Neighbor method...