In the previous section, the housing data underwent a lot of analytical pre-processing, and we are now ready to further analyze this. First, we begin with visualization. Since we have a lot of variables, the visualization on the R visual device is slightly difficult. As seen in earlier chapters, to visualize the random forests and other large, complex structures, we will initiate a PDF device and store the graphs in it. In the housing dataset, the main variable is the housing price and so we will first name the output variable SalePrice. We need to visualize the data in a way that facilitates the relationship between the numerous variables and the SalePrice. The independent variables can be either numeric or categorical. If the variables are numeric, a scatterplot will indicate the kind of relationship between the variable and the SalePrice regressand. If the independent variable is categorical/factor, we will visualize the boxplot at each level of the...
Hands-On Ensemble Learning with R
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Hands-On Ensemble Learning with R
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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.
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Current Title:
Hands-On Ensemble Learning with R
Table of Contents (17 chapters)
Hands-On Ensemble Learning with R
Contributors
Preface
Free Chapter
Introduction to Ensemble Techniques
Bootstrapping
Bagging
Random Forests
The Bare Bones Boosting Algorithms
Boosting Refinements
The General Ensemble Technique
Ensemble Diagnostics
Ensembling Regression Models
Ensembling Survival Models
Ensembling Time Series Models
What's Next?
Bibliography
Index
Customer Reviews