Book Image

Mastering SQL Server 2014 Data Mining

By : Amarpreet Singh Bassan, Debarchan Sarkar
Book Image

Mastering SQL Server 2014 Data Mining

By: Amarpreet Singh Bassan, Debarchan Sarkar

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>
Table of Contents (17 chapters)
Mastering SQL Server 2014 Data Mining
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Validating data models


In this section, we will be preparing the data mining models and validating them. This section will be a continuation of the last section where we finished creating the data source views for our mining structure.

Preparing the data mining models

Right-click on Mining Structures and then click on New. We will be presented with the following screen:

We now select the Microsoft Decision Trees algorithm, as shown here:

Then, we click on Next twice and reach the following screen:

Now, we select the case table as follows:

The case table is the one that has all the cases (essentially the set of values for different attributes) required to be provided to the model for making predictions. We will also use nested tables in the upcoming models and explain the nested tables as we encounter them.

We now select the model as the predictable column, as shown here:

We will also select the key column (in this case, CustomerKey). We see that the Suggest option is now enabled. Let's click on...