Applied Supervised Learning with R covers the complete process of developing applications with supervised machine learning algorithms that cater to your business needs. Your learning curve starts with developing your analytical thinking towards creating a problem statement using business inputs or domain research. You will learn many evaluation metrics that compare various algorithms, and you can then use these metrics to select the best algorithm for your problem. After finalizing the algorithm you want to use, you will study the hyperparameter optimization technique to fine tune your set of optimal parameters. To avoid overfitting your model, you will also be shown how to add various regularization terms.

When you complete the book, you will find yourself an expert at modelling a supervised machine learning algorithm that precisely fulfills your business need.