Summary
In this chapter, you were introduced to a class of machine learning algorithms called supervised learning algorithms, which can learn from well-labeled existing data. You explored the concepts of parametric and non-parametric learning algorithms and their pros and cons. Two major use cases of supervised learning algorithms called regression and classification were presented. Model training examples, along with code from Spark MLlib, were explored so that we could look at a few prominent types of regression and classification models. Tree ensemble methods, which improve the stability, accuracy, and performance of decision tree models by combining several models and preventing overfitting, were also presented.
Finally, you explored some real-world business applications of the various machine learning models presented in this chapter. We explained how supervised learning can be leveraged for business use cases, and working code samples were presented to help you train your...
