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

Running the OneR model

The syntax to the OneR model should be familiar. The outcome variable frisked is specified on the left side of the formula (~) and the features are specified on the right side. As you will recall, the metacharacter (.) designates that all features will be used as predictors:

model <- OneR(train_data, frisked ~ ., verbose = TRUE) 

The (partial) summary output displays the accuracy based upon selecting only one variable as a predictor along with its classification rate. The significant variables are starred.

The attribute and the accuracy metrics for the first 7 variables are shown next. Notice that once accuracy reaches 67.61% it does not decrease:

The call to the function is shown in the log, and the Decision Tree rules are displayed.

Interpreting the output

The output from the summary gives a good sense of the importance of each variable as an individual predictor. All of the accuracy measures range from 67.61% to 68.56%, so there is no obvious one single...