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    Hands-On Ensemble Learning with R

    Posted By: readerXXI
    Hands-On Ensemble Learning with R

    Hands-On Ensemble Learning with R : A Beginner's Guide to Combining
    the Power of Machine Learning Algorithms Using Ensemble Techniques

    by Prabhanjan Narayanachar Tattar
    English | 2018 | ISBN: 1788624149 | 376 Pages | PDF | 7.35 MB

    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.

    What you will learn:

    Carry out an essential review of re-sampling methods, bootstrap, and jackknife
    Explore the key ensemble methods: bagging, random forests, and boosting
    Use multiple algorithms to make strong predictive models
    Enjoy a comprehensive treatment of boosting methods
    Supplement methods with statistical tests, such as ROC
    Walk through data structures in classification, regression, survival, and time series data
    Use the supplied R code to implement ensemble methods
    Learn stacking method to combine heterogeneous machine learning models

    This book is for you if you are a data scientist or machine learning developer who wants to implement machine learning techniques by building ensemble models with the power of R. You will learn how to combine different machine learning algorithms to perform efficient data processing. Basic knowledge of machine learning techniques and programming knowledge of R would be an added advantage.