We're going to be using two of the prior datasets, the simulated data from Chapter 4, Advanced Feature Selection in Linear Models, and the customer satisfaction data from Chapter 3, Logistic Regression. We'll start by building a classification tree on the simulated data. This will help us to understand the basic principles of tree-based methods. Then, we'll move on to random forest and boosted trees applied to the customer satisfaction data. This exercise will provide an excellent comparison to the generalized linear models from before. Finally, I want to show you an interesting feature selection method using random forest, using the simulated data. By interesting, I mean it's a valuable technique to add to your feature selection arsenal, but I'll point out a couple of caveats for you to consider in practical application.
Advanced Machine Learning with R
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Advanced Machine Learning with R
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Overview of this book
R is one of the most popular languages when it comes to exploring the mathematical side of machine learning and easily performing computational statistics.
This Learning Path shows you how to leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. You’ll work through realistic projects such as building powerful machine learning models with ensembles to predict employee attrition. Next, you’ll explore different clustering techniques to segment customers using wholesale data and even apply TensorFlow and Keras-R for performing advanced computations. Each chapter will help you implement advanced machine learning algorithms using real-world examples. You’ll also be introduced to reinforcement learning along with its use cases and models. Finally, this Learning Path will provide you with a glimpse into how some of these black box models can be diagnosed and understood.
By the end of this Learning Path, you’ll be equipped with the skills you need to deploy machine learning techniques in your own projects.
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Advanced Machine Learning with R
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Table of Contents (30 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Free Chapter
Preparing and Understanding Data
Linear Regression
Logistic Regression
Advanced Feature Selection in Linear Models
K-Nearest Neighbors and Support Vector Machines
Tree-Based Classification
Neural Networks and Deep Learning
Creating Ensembles and Multiclass Methods
Cluster Analysis
Principal Component Analysis
Association Analysis
Time Series and Causality
Text Mining
Exploring the Machine Learning Landscape
Predicting Employee Attrition Using Ensemble Models
Implementing a Jokes Recommendation Engine
Sentiment Analysis of Amazon Reviews with NLP
Customer Segmentation Using Wholesale Data
Image Recognition Using Deep Neural Networks
Credit Card Fraud Detection Using Autoencoders
Automatic Prose Generation with Recurrent Neural Networks
Winning the Casino Slot Machines with Reinforcement Learning
Creating a Package
Other Books You May Enjoy
Index
Customer Reviews