Practical Machine Learning with R

Front of the book


Chapter 4, Introduction to neuralnet and Evaluation Methods, includes using a neural network to solve a classification problem with the neuralnet package; creating balanced partitions from a dataset, while decreasing leakage, with the groupdata2 package; and evaluating and selecting between models using cross-validation. Chapter 5, Linear and Logistic Regression Models, includes implementing and interpreting linear and logistic regression models; comparing linear and logistic regression models with cvms; implementing a random forest model; creating baseline evaluations with cvms; and selecting nondominated models, when metrics rank models differently. Chapters 1-3 were written by Brindha P. Jeyaraman, while chapter 6 was written by Monicah Wambugu.

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