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

Accuracy measures

Using the residuals, we can measure the error from the predicted and actual values based upon three popular accuracy measures:

  • Mean absolute error (MAE): This measure takes the mean of the absolute values of all of the errors (residuals)

  • Root-mean-squared error (RMSE): The root mean square error measures the error by first taking the mean of all of the squared errors, and then takes the square root of the mean, in order to revert back to the original scale. This is a standard statistical method of measuring errors.


Both MAE and RMSE are scale-dependent measures, which means that that they can be used to compare problems with similar scales. When comparing accuracy among models with different scales, other scale-independent measures such as MAPE should be used.

  • Mean percentage error (MAPE): This is the absolute difference between the actual and forecasted value, expressed as a percentage of the actual value. This is intuitively easy to understand and is a very popular measure...