Book Image

Applied Supervised Learning with R

By : Karthik Ramasubramanian, Jojo Moolayil
Book Image

Applied Supervised Learning with R

By: Karthik Ramasubramanian, Jojo Moolayil

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.
Table of Contents (12 chapters)
Applied Supervised Learning with R
Preface

Elastic Net Regression


Elastic Net combines the penalty terms of ridge and LASSO regression to avoid the overdependence on data for variable selection (coefficient values tending to zero by which highly correlated variables are kept in check). Elastic Net minimizes the following loss function:

Where the parameter α controls the right mix between ridge and LASSO.

In summary, if a model has many predictor variables or correlated variables, introducing the regularization term helps in reducing the variance and increase bias optimally, thus bringing the right balance of model complexity and error. Figure 4.16 provides a flow diagram to help one choose between multiple, ridge, LASSO, and elastic net regression:

Figure 4.16: Selection criteria to choose between multiple, ridge, LASSO, and elastic net regression

Exercise 58: Elastic Net Regression

In this exercise, we will perform elastic net regression on the Beijing PM2.5 dataset.

Perform the following steps to complete the exercise:

  1. Let's first set...