In this chapter, we looked at using probabilistic linear models to predict a qualitative response with two generalized linear model methods: logistic regression, and multivariate adaptive regression splines. We explored using the weight of information and information value as a technique to do univariate feature selection. We covered the concept of finding the proper probability threshold to minimize classification error. Additionally, we began the process of using various performance metrics such as AUC, log-loss, and ROC charts to explore model selection visually and statistically. These metrics proved to be more informative than just pure accuracy, especially in a situation where class labels are highly imbalanced. In the next chapter, we'll cover regularization methods for feature selection, and how it can be used in training your algorithms. We'll see how we can create a dataset. We'll know about ridge regression and dive deeper in feature selection.
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