Feature Engineering and Selection: A Practical Approach for Predictive Models (Chapman & Hall/CRC Data Science Series)
Product Description
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.
Technical Specifications
You might also like
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
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists
Applied Predictive Modeling
Machine Learning: An Applied Mathematics Introduction
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