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

Polynomial Regression


Often in real-world data, the response variable and the predictor variable don't have a linear relationship, and we may need a nonlinear polynomial function to fit the data. Various scatterplot-like residual versus each predictor and residual versus fitted values reveal the violation of linearity if any, which could potentially help in identifying the need for introducing the quadratic or cubic term in the equation. The following function is a generic polynomial equation:

Where k is the degree of the polynomial. For k=2, f(X) is called quadratic and h=4 is called cubic. Note that polynomial regression is still considered linear regression since it is still linear in coefficient .

Before revisiting the Beijing PM2.5 example, let's understand how polynomial regression works using simulated data from the quadratic equation we introduced in the Linear Regression section.

Exercise 55: Performing Uniform Distribution Using the runif() Function

In this exercise, we will generate...