Book Image

Machine Learning with Qlik Sense

By : Hannu Ranta
Book Image

Machine Learning with Qlik Sense

By: Hannu Ranta

Overview of this book

The ability to forecast future trends through data prediction, coupled with the integration of ML and AI, has become indispensable to global enterprises. Qlik, with its extensive machine learning capabilities, stands out as a leading analytics platform enabling businesses to achieve exhaustive comprehension of their data. This book helps you maximize these capabilities by using hands-on illustrations to improve your ability to make data-driven decisions. You’ll begin by cultivating an understanding of machine learning concepts and algorithms, and build a foundation that paves the way for subsequent chapters. The book then helps you navigate through the process of framing machine learning challenges and validating model performance. Through the lens of Qlik Sense, you'll explore data preprocessing and analysis techniques, as well as find out how to translate these techniques into pragmatic machine learning solutions. The concluding chapters will help you get to grips with advanced data visualization methods to facilitate a clearer presentation of findings, complemented by an array of real-world instances to bolster your skillset. By the end of this book, you’ll have mastered the art of machine learning using Qlik tools and be able to take your data analytics journey to new heights.
Table of Contents (17 chapters)
Part 1:Concepts of Machine Learning
Part 2: Machine learning algorithms and models with Qlik
Part 3: Case studies and best practices

Monitoring and debugging models

Debugging a model during development is a crucial development step. With Advanced Analytics Integration in on-premises environments, we have several options to debug our model and figure out how it is performing.

The first and most logical place to start debugging in an interactive scenario is to look at the chart output. If there is something wrong with the code, you will get an error message here. In the following example, we can see that a library called forecast is missing from the environment:

Figure 7.8: Error message in chart

Figure 7.8: Error message in chart

Sometimes, you may need more comprehensive information or debug prints from the actual R code. Since R is running as a service, there is no easy way to get debug prints during execution. You can, however, use file writing. Returning to our previous Rserve example, adding the following code will produce a file called debug.txt in our Rserve home folder (the added code is shown in bold):