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

Summary


Time series data poses new challenges and complexities. The chapter began with an introduction to important and popular datasets. We looked at different time series and their intricacies. Visualization of time series provides great insight, and the time series plots, along with the seasonal plot, are complementarily used for clear ideas and niche implementations. Accuracy metrics are different for the time series, and we looked at more than a handful of these. The concepts of ACF and PACF are vital in model identification, and seasonal components are also important to the modeling of time series. We also saw that different models express different datasets, and the degree of variation is something similar to the usual regression problems. The bagging of time series (ets only) reduces the variance of the forecasts. Combining heterogeneous base learners was discussed in the concluding section. The next chapter is the concluding chapter. We will summarize the main takeaways from the...