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Smarter Decisions - The Intersection of Internet of Things and Decision Science

You're reading from   Smarter Decisions - The Intersection of Internet of Things and Decision Science A comprehensive guide for solving IoT business problems using decision science

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Product type Paperback
Published in Jul 2016
Publisher Packt
ISBN-13 9781785884191
Length 392 pages
Edition 1st Edition
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Author (1):
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Jojo Moolayil Jojo Moolayil
Author Profile Icon Jojo Moolayil
Jojo Moolayil
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Table of Contents (10) Chapters Close

Preface 1. IoT and Decision Science FREE CHAPTER 2. Studying the IoT Problem Universe and Designing a Use Case 3. The What and Why - Using Exploratory Decision Science for IoT 4. Experimenting Predictive Analytics for IoT 5. Enhancing Predictive Analytics with Machine Learning for IoT 6. Fast track Decision Science with IoT 7. Prescriptive Science and Decision Making 8. Disruptions in IoT 9. A Promising Future with IoT

Logistic Regression - Predicting a categorical outcome

Let's shift our focus to building a predictive model that will now take a different step. We started by solving the prediction problem that can predict a continuous outcome, but we didn't achieve great results. John's team requires a solution that they can leverage to predict the end quality of the detergent being manufactured. It could be achieved in multiple ways; the first one was to predict the most critical output quality parameter and the second was to predict the actual end outcome, Good or Bad. Both the methods have their own advantages and disadvantages. Predicting the continuous outcome, Output Quality Parameter 2, actually gives us a sneak peek to understand the actual quantified deviation from the benchmark, say below or above 60%. Such crisp information aids the technician in taking more accurate corrective countermeasures.

On the other hand, predicting the categorical outcome, Good/Bad Quality, has its interpretational...

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