The Microsoft Neural Network algorithm uses a combination of each state of the input variable and each state of the output variable to form a series of networks or neurons. It uses the input to calculate the probabilities for a particular outcome. The complex structure of the algorithm lends its usefulness for complex analysis, such as stock movement, currency fluctuation, and so on. The Microsoft Neural Network algorithm is based on a multilayer perceptron network, which consists of multiple layers, namely the input layer, hidden layer (which is optional), and output layer. We will look at the parameters for the algorithm and also how their alteration affects the output of the predictable values later in this chapter. The three layers of the model are briefly described as follows:
Mastering SQL Server 2014 Data Mining
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Mastering SQL Server 2014 Data Mining
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Overview of this book
<p>Whether you are new to data mining or are a seasoned expert, this book will provide you with the skills you need to successfully create, customize, and work with Microsoft Data Mining Suite. Starting with the basics, this book will cover how to clean the data, design the problem, and choose a data mining model that will give you the most accurate prediction.</p>
<p>Next, you will be taken through the various classification models such as the decision tree data model, neural network model, as well as Naïve Bayes model. Following this, you'll learn about the clustering and association algorithms, along with the sequencing and regression algorithms, and understand the data mining expressions associated with each algorithm. With ample screenshots that offer a step-by-step account of how to build a data mining solution, this book will ensure your success with this cutting-edge data mining system.</p>
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Table of Contents (17 chapters)
Mastering SQL Server 2014 Data Mining
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Identifying, Staging, and Understanding Data
Data Model Preparation and Deployment
Tools of the Trade
Preparing the Data
Classification Models
Segmentation and Association Models
Sequence and Regression Models
Data Mining Using Excel and Big Data
Tuning the Models
Troubleshooting
Index
Customer Reviews