LEADER 03694nam 2200445 450 001 9910494595803321 005 20201001132631.7 010 $a1-83855-716-4 035 $a(CKB)4100000008402237 035 $a(MiAaPQ)EBC5784240 035 $a(CaSebORM)9781838556334 035 $a(PPN)23842068X 035 $a(EXLCZ)994100000008402237 100 $a20190622d2019 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aApplied supervised learning with R $euse machine learning libraries of R to build models that solve business problems and predict future trends. /$fKarthik Ramasubramanian and Jojo Moolayil 205 $a1st edition 210 1$aBirmingham, England :$cPackt Publishing,$d2019. 215 $a1 online resource (503 pages) 311 $a1-83855-633-8 330 $aLearn 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. Key Features Study supervised learning algorithms by using real-world datasets Fine tune optimal parameters with hyperparameter optimization Select the best algorithm using the model evaluation framework Book Description 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 the advanced skills you need for modeling a supervised machine learning algorithm that precisely fulfills your business needs. What you will learn 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 Who this book is for This book is specially designed for beginner and intermediate-level data analysts, data scientists, and data engineers who want to explore different methods of supervised machine learning and its use cases. Some background in statistics, probability, calculus, linear algebra, and programming will help you thoroughly understand and follow the concepts covered in this book. 606 $aMachine learning 606 $aR (Computer program language) 608 $aElectronic books. 615 0$aMachine learning. 615 0$aR (Computer program language) 676 $a006.31 700 $aRamasubramanian$b Karthik$0897301 702 $aMoolayil$b Jojo 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910494595803321 996 $aApplied supervised learning with R$92490906 997 $aUNINA