Applied Supervised Learning with R
- Paperback: 502 pages
- Publisher: WOW! eBook (May 31, 2019)
- Language: English
- ISBN-10: 1838556338
- ISBN-13: 978-1838556334
Applied Supervised Learning with R: Learn the ropes of supervised machine learning with R by studying popular real-world use cases, and understand how it drives object detection in driverless cars, customer churn, and loan default prediction
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.
- Develop analytical thinking to precisely identify a business problem
- Wrangle data with dplyr, tidyr, and reshape2
- Visualize data with ggplot2
- Validate your supervised machine learning model using k-fold
- Optimize hyperparameters with grid and random search, and Bayesian optimization
- Deploy your model on Amazon Web Services (AWS) Lambda with plumber
- Improve your model’s performance with feature selection and dimensionality reduction
By the end of this Applied Supervised Learning with R book, you will have gained the advanced skills you need for modeling a supervised machine learning algorithm that precisely fulfills your business needs.