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4.8
54 reviewsHow to avoid common problems, such as dealing with “dirty” data and factor variables with large numbers of levels
A look at typical misconceptions, such as dealing with unbalanced data
Exploration of the famous Bias-Variance Tradeoff, central to machine learning, and how it plays out in practice for each machine learning method
Dozens of illustrative examples involving real datasets of varying size and field of application
Standard R packages are used throughout, with a simple wrapper interface to provide convenient access.
After finishing this book, you will be well equipped to start applying machine learning techniques to your own datasets.