Book Image

Machine Learning with R Quick Start Guide

By : Iván Pastor Sanz
Book Image

Machine Learning with R Quick Start Guide

By: Iván Pastor Sanz

Overview of this book

Machine Learning with R Quick Start Guide takes you on a data-driven journey that starts with the very basics of R and machine learning. It gradually builds upon core concepts so you can handle the varied complexities of data and understand each stage of the machine learning pipeline. From data collection to implementing Natural Language Processing (NLP), this book covers it all. You will implement key machine learning algorithms to understand how they are used to build smart models. You will cover tasks such as clustering, logistic regressions, random forests, support vector machines, and more. Furthermore, you will also look at more advanced aspects such as training neural networks and topic modeling. By the end of the book, you will be able to apply the concepts of machine learning, deal with data-related problems, and solve them using the powerful yet simple language that is R.
Table of Contents (9 chapters)

Summary

In this chapter, we used different models and algorithms to try and optimize our model. All of the algorithms obtained good results. This would not have been the case in other problems. You can try using different algorithms in your problems and test the best combinations of parameters to solve your specific problem. A combination of different algorithms or ensembles might be a good option as well.

In the next chapter, we will continue by looking at other real problems—specifically, data visualization of economic imbalances in European countries.