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

Boosting algorithms and Naive Bayes

Boosting is a machine learning technique that involves creating an ensemble of weak learners to form a strong learner. The idea behind boosting algorithms is to iteratively train models on the data, where each new model attempts to correct the errors of the previous model. Boosting algorithms are widely used in supervised learning tasks, such as classification and regression.

There are several key types of boosting algorithms:

  • AdaBoost (Adaptive Boosting): AdaBoost is one of the earliest and most popular boosting algorithms. It starts by training a base classifier on the entire dataset and then sequentially trains additional classifiers on the samples that the previous classifiers got wrong. The final prediction is made by taking a weighted sum of the predictions of all the classifiers.
  • Gradient Boosting: Gradient Boosting is another popular boosting algorithm that works by iteratively adding new models to the ensemble, each trained...