Decision trees is a commonly used technique in data mining to create a model that predicts the value of a target (or dependent variable) based on the values of several input (or independent variables). There is a variety of decision tree algorithms available with small changes here and there. We will be using a very popular version of a decision tree called Classification and Regression Trees (CART). It was introduced in 1984 by Leo Breiman, Jerome Friedman, Richard Olshen, and Charles Stone as an umbrella term to refer to classification and regression types of decision trees. Using decision trees, we can predict either a categorical variable or continuous variable. Based on the type of dependent variable, we use a regression tree (for a continuous outcome variable) or classification tree (for a categorical outcome). The CART has a small variation in the internal working of the algorithm. For our current exercise, we will be using regression trees. Later, we'll look into the...
Smarter Decisions - The Intersection of Internet of Things and Decision Science
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Smarter Decisions - The Intersection of Internet of Things and Decision Science
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Overview of this book
With an increasing number of devices getting connected to the Internet, massive amounts of data are being generated that can be used for analysis. This book helps you to understand Internet of Things in depth and decision science, and solve business use cases. With IoT, the frequency and impact of the problem is huge. Addressing a problem with such a huge impact requires a very structured approach.
The entire journey of addressing the problem by defining it, designing the solution, and executing it using decision science is articulated in this book through engaging and easy-to-understand business use cases. You will get a detailed understanding of IoT, decision science, and the art of solving a business problem in IoT through decision science.
By the end of this book, you’ll have an understanding of the complex aspects of decision making in IoT and will be able to take that knowledge with you onto whatever project calls for it
Table of Contents (15 chapters)
Smarter Decisions – The Intersection of Internet of Things and Decision Science
Credits
About the Author
About the Reviewer
eBooks, discount offers, and more
Preface
Free Chapter
IoT and Decision Science
Studying the IoT Problem Universe and Designing a Use Case
The What and Why - Using Exploratory Decision Science for IoT
Experimenting Predictive Analytics for IoT
Enhancing Predictive Analytics with Machine Learning for IoT
Fast track Decision Science with IoT
Prescriptive Science and Decision Making
Disruptions in IoT
Customer Reviews