In this chapter, we used the mlr and OpenML packages from R to build an entire machine learning workflow for solving a multilabel semantic scene classification problem. The mlr package offered a rich collection of machine learning algorithms and evaluation measures that helped us in quick implementation and facilitated a faster experimentation process to get the best model for the problem. The package also offered many wrapper functions to handle the multilabel problem. Building real-world machine learning models using a robust framework such as the one in mlr helps in speeding the implementation and provides a structure to the complete project. Further, using OpenML, we could reproduce a research work using the already available dataset and code, and then modify it according to our need. Such a platform offers the ability to collaborate at scale with researchers all over the world. At the end, we could also upload our own machine learning flows with others for them to pick it up...
Applied Supervised Learning with R
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Applied Supervised Learning with R
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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.
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Current Title:
Applied Supervised Learning with R
Table of Contents (12 chapters)
Applied Supervised Learning with R
Preface
Free Chapter
R for Advanced Analytics
Exploratory Analysis of Data
Introduction to Supervised Learning
Regression
Classification
Feature Selection and Dimensionality Reduction
Model Improvements
Model Deployment
Capstone Project - Based on Research Papers
Appendix
Customer Reviews