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IBM SPSS Modeler Essentials电子书

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29人正在读 | 0人评论 6.2

作       者:Jesus Salcedo,Keith McCormick

出  版  社:Packt Publishing

出版时间:2017-12-26

字       数:21.8万

所属分类: 进口书 > 外文原版书 > 电脑/网络

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Get to grips with the fundamentals of data mining and predictive analytics with IBM SPSS Modeler About This Book ? Get up–and-running with IBM SPSS Modeler without going into too much depth. ? Identify interesting relationships within your data and build effective data mining and predictive analytics solutions ? A quick, easy–to-follow guide to give you a fundamental understanding of SPSS Modeler, written by the best in the business Who This Book Is For This book is ideal for those who are new to SPSS Modeler and want to start using it as quickly as possible, without going into too much detail. An understanding of basic data mining concepts will be helpful, to get the best out of the book. What You Will Learn ? Understand the basics of data mining and familiarize yourself with Modeler’s visual programming interface ? Import data into Modeler and learn how to properly declare metadata ? Obtain summary statistics and audit the quality of your data ? Prepare data for modeling by selecting and sorting cases, identifying and removing duplicates, combining data files, and modifying and creating fields ? Assess simple relationships using various statistical and graphing techniques ? Get an overview of the different types of models available in Modeler ? Build a decision tree model and assess its results ? Score new data and export predictions In Detail IBM SPSS Modeler allows users to quickly and efficiently use predictive analytics and gain insights from your data. With almost 25 years of history, Modeler is the most established and comprehensive Data Mining workbench available. Since it is popular in corporate settings, widely available in university settings, and highly compatible with all the latest technologies, it is the perfect way to start your Data Science and Machine Learning journey. This book takes a detailed, step-by-step approach to introducing data mining using the de facto standard process, CRISP-DM, and Modeler’s easy to learn “visual programming” style. You will learn how to read data into Modeler, assess data quality, prepare your data for modeling, find interesting patterns and relationships within your data, and export your predictions. Using a single case study throughout, this intentionally short and focused book sticks to the essentials. The authors have drawn upon their decades of teaching thousands of new users, to choose those aspects of Modeler that you should learn first, so that you get off to a good start using proven best practices. This book provides an overview of various popular data modeling techniques and presents a detailed case study of how to use CHAID, a decision tree model. Assessing a model’s performance is as important as building it; this book will also show you how to do that. Finally, you will see how you can score new data and export your predictions. By the end of this book, you will have a firm understanding of the basics of data mining and how to effectively use Modeler to build predictive models. Style and approach This book empowers users to build practical & accurate predictive models quickly and intuitively. With the support of the advanced analytics users can discover hidden patterns and trends.This will help users to understand the factors that influence them, enabling you to take advantage of business opportunities and mitigate risks.
目录展开

Title Page

Copyright

IBM SPSS Modeler Essentials

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Why subscribe?

Customer Feedback

Dedication

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

Introduction to Data Mining and Predictive Analytics

Introduction to data mining

CRISP-DM overview

Business Understanding

Data Understanding

Data Preparation

Modeling

Evaluation

Deployment

Learning more about CRISP-DM

The data mining process (as a case study)

Summary

The Basics of Using IBM SPSS Modeler

Introducing the Modeler graphic user interface

Stream canvas

Palettes

Modeler menus

Toolbar

Manager tabs

Project window

Building streams

Mouse buttons

Adding nodes

Editing nodes

Deleting nodes

Building a stream

Connecting nodes

Deleting connections

Modeler stream rules

Help options

Help menu

Dialog help

Summary

Importing Data into Modeler

Data structure

Var. File source node

Var. File source node File tab

Var. File source node Data tab

Var. File source node Filter tab

Var. File source node Types tab

Var. File source node Annotations tab

Viewing data

Excel source node

Database source node

Levels of measurement and roles

Summary

Data Quality and Exploration

Data Audit node options

Data Audit node results

The Quality tab

Missing data

Ways to address missing data

Defining missing values in the Type node

Imputing missing values with the Data Audit node

Summary

Cleaning and Selecting Data

Selecting cases

Expression Builder

Sorting cases

Identifying and removing duplicate cases

Reclassifying categorical values

Summary

Combining Data Files

Combining data files with the Append node

Removing fields with the Filter node

Combining data files with the Merge node

The Filter tab

The Optimization tab

Summary

Deriving New Fields

Derive – Formula

Derive – Flag

Derive – Nominal

Derive – Conditional

Summary

Looking for Relationships Between Fields

Relationships between categorical fields

Distribution node

Matrix node

Relationships between categorical and continuous fields

Histogram node

Means node

Relationships between continuous fields

Plot node

Statistics node

Summary

Introduction to Modeling Options in IBM SPSS Modeler

Classification

Categorical targets

Numeric targets

The Auto nodes

Data reduction modeling nodes

Association

Segmentation

Choosing between models

Summary

Decision Tree Models

Decision tree theory

CHAID theory

How CHAID processes different types of input variables

Stopping rules

Building a CHAID Model

Partition node

Overfitting

CHAID dialog options

CHAID results

Summary

Model Assessment and Scoring

Contrasting model assessment with the Evaluation phase

Model assessment using the Analysis node

Modifying CHAID settings

Model comparison using the Analysis node

Model assessment and comparison using the Evaluation node

Scoring new data

Exporting predictions

Summary

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