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Machine Learning with Qlik Sense

You're reading from   Machine Learning with Qlik Sense Utilize different machine learning models in practical use cases by leveraging Qlik Sense

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Product type Paperback
Published in Oct 2023
Publisher Packt
ISBN-13 9781805126157
Length 242 pages
Edition 1st Edition
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Author (1):
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Hannu Ranta Hannu Ranta
Author Profile Icon Hannu Ranta
Hannu Ranta
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Table of Contents (17) Chapters Close

Preface 1. Part 1:Concepts of Machine Learning
2. Chapter 1: Introduction to Machine Learning with Qlik FREE CHAPTER 3. Chapter 2: Machine Learning Algorithms and Models with Qlik 4. Chapter 3: Data Literacy in a Machine Learning Context 5. Chapter 4: Creating a Good Machine Learning Solution with the Qlik Platform 6. Part 2: Machine learning algorithms and models with Qlik
7. Chapter 5: Setting Up the Environments 8. Chapter 6: Preprocessing and Exploring Data with Qlik Sense 9. Chapter 7: Deploying and Monitoring Machine Learning Models 10. Chapter 8: Utilizing Qlik AutoML 11. Chapter 9: Advanced Data Visualization Techniques for Machine Learning Solutions 12. Part 3: Case studies and best practices
13. Chapter 10: Examples and Case Studies 14. Chapter 11: Future Direction 15. Index 16. Other Books You May Enjoy

Regression models

Regression models are a type of supervised machine-learning model used to predict continuous numerical values for a target variable based on one or more input variables. In other words, regression models are used to estimate the relationships between the input variables and the output variable.

There are various types of regression models used in machine learning, some of which include the following:

  • Linear Regression: This is a type of regression model that assumes a linear relationship between the input variables and the output variable.
  • Polynomial Regression: This is a type of regression model that assumes a polynomial relationship between the input variables and the output variable.
  • Logistic Regression: This is a type of regression model used to predict binary or categorical outcomes. It estimates the probability of an event occurring based on the input variables.
  • Ridge Regression: This is a type of linear regression model that uses regularization...
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