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

Line Charts


A line chart or line graph is a type of chart that displays information as a series of data points called markers connected by straight line segments.

ggplot uses an elegant geom() method, which helps in quickly switching between two visual objects. In the previous example, we saw geom_point() for the scatterplot. In line charts, the observations are connected by a line in the order of the variable on the x-axis. The shaded area surrounding the line represents the 95% confidence interval, that is, there is 95% confidence that the actual regression line lies within the shaded area. We will discuss more on this idea in Chapter 4, Regression.

In the following plot, we show the line chart of age and bank balance for single, married, and divorced individuals. It is not clear whether there is some trend, but one can see the pattern among the three categories:

ggplot(data = df_bank_detail) +
  geom_smooth(mapping = aes(x = age, y = balance, linetype = marital))
## 'geom_smooth()' using method = 'gam'

Figure 1.10: Line graph of age and balance