Book Image

Hands-On Ensemble Learning with R

By : Prabhanjan Narayanachar Tattar
Book Image

Hands-On Ensemble Learning with R

By: Prabhanjan Narayanachar Tattar

Overview of this book

Ensemble techniques are used for combining two or more similar or dissimilar machine learning algorithms to create a stronger model. Such a model delivers superior prediction power and can give your datasets a boost in accuracy. Hands-On Ensemble Learning with R begins with the important statistical resampling methods. You will then walk through the central trilogy of ensemble techniques – bagging, random forest, and boosting – then you'll learn how they can be used to provide greater accuracy on large datasets using popular R packages. You will learn how to combine model predictions using different machine learning algorithms to build ensemble models. In addition to this, you will explore how to improve the performance of your ensemble models. By the end of this book, you will have learned how machine learning algorithms can be combined to reduce common problems and build simple efficient ensemble models with the help of real-world examples.
Table of Contents (17 chapters)
Hands-On Ensemble Learning with R
Contributors
Preface
12
What's Next?
Index

Time series visualization


The main characteristic of time series data is that the observations are taken at regular intervals. A plot of the time series values (the y axis) against the time itself (x axis) is of great importance and gives away many structural insights. A time series plot is not merely a scatterplot with time as the x axis. The time is non-decreasing and hence it has more importance and meaning in a time series plot than the mere x axis in a scatterplot. For instance, lines can connect the points of a time series plot that will indicate the path of the time series, and such a connection would be meaningless in the scatterplot, which would be all over the place. The path will generally indicate the trend and as such, shows in which direction the series will go next. Changes in time series are easily depicted in the plot. We will now visualize the different time series.

The plot.ts function is central to the scheme of visualization here. An external graphical device of appropriate...