In this chapter, we saw various feature selection and reduction techniques. The three main topics covered in this chapter were: Feature Engineering, Feature Selection, and Feature Reduction. The latter two have the same purpose of shrinking the number of features; however, the techniques used are completely different. Feature Engineering focuses on transforming variables into a new form that either helps in improving the model performance or makes the variable be in compliance with model assumption. An example is the linearity assumption in the linear regression model, where we typically could square or cube the variables and the skewness in data distribution, which could be addressed using log transformation. Feature Selection and Feature Reduction help in providing the best feature set or the best representation of the feature set, which improves model performance. Most importantly, both techniques shrink the feature space, which drastically improves the model training time without...
Applied Supervised Learning with R
By :
Applied Supervised Learning with R
By:
Overview of this book
R provides excellent visualization features that are essential for exploring data before using it in automated learning.
Applied Supervised Learning with R helps you cover the complete process of employing R to develop applications using supervised machine learning algorithms for your business needs. The book starts by helping you develop your analytical thinking to create a problem statement using business inputs and domain research. You will then learn different evaluation metrics that compare various algorithms, and later progress to using these metrics to select the best algorithm for your problem. After finalizing the algorithm you want to use, you will study the hyperparameter optimization technique to fine-tune your set of optimal parameters. The book demonstrates how you can add different regularization terms to avoid overfitting your model.
By the end of this book, you will have gained the advanced skills you need for modeling a supervised machine learning algorithm that precisely fulfills your business needs.
Related Content you might be interested in
Current Title:
Applied Supervised Learning with R
Table of Contents (12 chapters)
Applied Supervised Learning with R
Preface
Free Chapter
R for Advanced Analytics
Exploratory Analysis of Data
Introduction to Supervised Learning
Regression
Classification
Feature Selection and Dimensionality Reduction
Model Improvements
Model Deployment
Capstone Project - Based on Research Papers
Appendix
Customer Reviews