Book Image

Implementing Splunk 7, Third Edition - Third Edition

Book Image

Implementing Splunk 7, Third Edition - Third Edition

Overview of this book

Splunk is the leading platform that fosters an efficient methodology and delivers ways to search, monitor, and analyze growing amounts of big data. This book will allow you to implement new services and utilize them to quickly and efficiently process machine-generated big data. We introduce you to all the new features, improvements, and offerings of Splunk 7. We cover the new modules of Splunk: Splunk Cloud and the Machine Learning Toolkit to ease data usage. Furthermore, you will learn to use search terms effectively with Boolean and grouping operators. You will learn not only how to modify your search to make your searches fast but also how to use wildcards efficiently. Later you will learn how to use stats to aggregate values, a chart to turn data, and a time chart to show values over time; you'll also work with fields and chart enhancements and learn how to create a data model with faster data model acceleration. Once this is done, you will learn about XML Dashboards, working with apps, building advanced dashboards, configuring and extending Splunk, advanced deployments, and more. Finally, we teach you how to use the Machine Learning Toolkit and best practices and tips to help you implement Splunk services effectively and efficiently. By the end of this book, you will have learned about the Splunk software as a whole and implemented Splunk services in your tasks at projects
Table of Contents (19 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

Validation


Validation of a forecast model involves training the model with a portion of data (referred to as your training data) and then testing the model with a different portion (referred to as your test data).

For forecasting tasks, the training data is a prefix of the data and the test data is a suffix of the data that is withheld to compare against the forecasts.

Validating a trained model with the test set can be performed several ways, depending on the type of model. Each assistant provides methods in the Validate section, which is displayed after you train a model.

Deployment

A model is ready to be deployed after you have validated it and are comfortable with its performance. Deployment actions are usually categorized as:

  • Generate a forecast to use directly or as input to other analytics applications
  • Detect outliners and anomalies to help improve the overall process
  • Trigger an action or alert of a needed decision

The Splunk Machine Learning Toolkit makes deploying and sharing the results...