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

Highly Correlated Variables


Generally, two highly correlated variables likely contribute to the prediction ability of the model, which makes one redundant. For example, if we have a dataset with age, height, and BMI as variables, we know that BMI is a function of age and height and it will always be highly correlated with the other two. If it's not, then something is wrong with the BMI calculation. In such cases, one might decide to remove the other two. However, it is always not this straight. In certain cases, a pair of variables might be highly correlated, but it is not easy to interpret why that is the case. In such cases, one can randomly drop one of the two.

Exercise 82: Plotting a Correlated Matrix

In this exercise, we will compute the correlation between a pair of variables and draw a correlation plot using the corrplot package.

Perform the following steps to complete the exercise:

  1. Import the required libraries using the following command:

    library(mlbench)
    library(caret)

    The output is...