Book Image

Applied Supervised Learning with R

By : Karthik Ramasubramanian, Jojo Moolayil
Book Image

Applied Supervised Learning with R

By: Karthik Ramasubramanian, Jojo Moolayil

Overview of this book

R provides excellent visualization features that are essential for exploring data before using it in automated learning. Applied Supervised Learning with R helps you cover the complete process of employing R to develop applications using supervised machine learning algorithms for your business needs. The book starts by helping you develop your analytical thinking to create a problem statement using business inputs and domain research. You will then learn different evaluation metrics that compare various algorithms, and later progress to using 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. The book demonstrates how you can add different regularization terms to avoid overfitting your model. By the end of this book, you will have gained the advanced skills you need for modeling a supervised machine learning algorithm that precisely fulfills your business needs.
Table of Contents (12 chapters)
Applied Supervised Learning with R
Preface

K-Fold Cross-Validation


This technique is the most recommended approach for model evaluation. In this technique, we partition the data into k groups and use k-1 groups for training and the remainder (1 group) for validation. The process is repeated k times, where a new group is used for validation in each successive iteration, and therefore, each group is used for testing at one point of time. The overall results are the average error estimates across k iterations.

k-fold cross-validations, therefore, overcomes the drawbacks of the holdout technique by mitigating the perils associated with the nature of split as each data point is tested once over the book of k iterations. The variance of the model is reduced as the value of k increases. The most common values used for k are 5 or 10. The major drawback of this technique is that it trains the model k times (for k iterations). Therefore, the total compute time required for the model to train and validate is approximately k times the holdout...