Book Image

Practical Predictive Analytics

By : Ralph Winters
Book Image

Practical Predictive Analytics

By: Ralph Winters

Overview of this book

This is the go-to book for anyone interested in the steps needed to develop predictive analytics solutions with examples from the world of marketing, healthcare, and retail. We'll get started with a brief history of predictive analytics and learn about different roles and functions people play within a predictive analytics project. Then, we will learn about various ways of installing R along with their pros and cons, combined with a step-by-step installation of RStudio, and a description of the best practices for organizing your projects. On completing the installation, we will begin to acquire the skills necessary to input, clean, and prepare your data for modeling. We will learn the six specific steps needed to implement and successfully deploy a predictive model starting from asking the right questions through model development and ending with deploying your predictive model into production. We will learn why collaboration is important and how agile iterative modeling cycles can increase your chances of developing and deploying the best successful model. We will continue your journey in the cloud by extending your skill set by learning about Databricks and SparkR, which allow you to develop predictive models on vast gigabytes of data.
Table of Contents (19 chapters)
Title Page
About the Author
About the Reviewers
Customer Feedback

Step 5 evaluation

Model evaluation deals with how accurate or useful the model you have just developed is or will be in the future. Model evaluation can take different forms. Some are more subjective and are domain oriented, such as placing it under the scrutiny of experts in your field, and some are more technically oriented. There are many metrics and procedures available to assess a model. At the basic level, you have many statistics (some of them with acronyms known as AIC, BIC, and AUC) which purport to convey the goodness of a model in a single metric. However, these metrics by themselves are unable to convey the purpose and application of a predictive model to a larger audience and often these metrics are in conflict. Some context is needed. Some would argue that one could also develop a perfectly good predictive model and then be unable to convey its purpose and application to a larger audience. In my opinion, that is a bad model, regardless of how well an evaluation metric fits...