Feature Engineering and Selection: A Practical Approach for Predictive Models (Chapman & Hall/CRC Data Science Series)

The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.

• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
• Advanced R, Second Edition (Chapman & Hall/CRC The R Series)
• Practical Time Series Analysis: Prediction with Statistics and Machine Learning
• Applied Predictive Modeling
• Machine Learning: An Applied Mathematics Introduction
• Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists
• Feature Engineering Made Easy: Identify unique features from your dataset in order to build powerful machine learning systems
• Mastering Spark with R: The Complete Guide to Large-Scale Analysis and Modeling
• Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures
• Deep Learning from Scratch: Building with Python from First Principles