Atnaujinkite slapukų nuostatas

Pmml in Action [Minkštas viršelis]

2.83/5 (11 ratings by Goodreads)
, ,
  • Formatas: Paperback / softback, 190 pages, aukštis x plotis x storis: 156x234x10 mm, weight: 272 g
  • Išleidimo metai: 18-May-2010
  • Leidėjas: Createspace
  • ISBN-10: 1452858268
  • ISBN-13: 9781452858265
Pmml in ActionDidesnis paveikslėlis
  • Formatas: Paperback / softback, 190 pages, aukštis x plotis x storis: 156x234x10 mm, weight: 272 g
  • Išleidimo metai: 18-May-2010
  • Leidėjas: Createspace
  • ISBN-10: 1452858268
  • ISBN-13: 9781452858265
Pastovi nuoroda: https://www.kriso.lt/db/9781452858265.html
PMML (Predictive Model Markup Language) is the de facto standard used to represent and share predictive analytic solutions between applications. This enables data mining scientists and users alike to easily build, visualize, and deploy their solutions using different platforms and systems. This book presents PMML from a practical perspective. It contains a variety of code snippets so that concepts are made clear through the use of examples."PMML in Action" is a great way to learn how to represent your predictive models through a mature open standard. The book is divided into six parts, taking you into a PMML journey in which language elements and attributes are used to represent not only modeling techniques but also data transformations. With PMML, users benefit from a single and concise standard to represent data and models, thus avoiding the need for custom code and proprietary solutions.You too can join the PMML movement! Unleash the power of predictive analytics and data mining today!
Foreword vii
Preface ix
1 How to Use this Guide
ix
2 PMML Examples
xi
3 Acknowledgments
xi
4 About the Authors
xii
5 About Zementis, Inc
xiii
I Introduction
1(34)
1 Introduction to PMML
3(14)
1.1 Data Mining and Predictive Analytics
3(1)
1.1.1 Benefiting from Open Standards and Cloud Computing
4(1)
1.1.2 The Issue of Operational Deployment
4(1)
1.2 The Predictive Model Markup Language
5(3)
1.2.1 One Standard, One Process
8(1)
1.2.2 PMML Release History
9(2)
1.2.3 PMML Products
11(2)
1.2.4 PMML Converter
13(1)
1.2.5 PMML and Excel
14(1)
1.2.6 PMML On-Line Discussion Groups
15(1)
1.2.7 PMML References
16(1)
2 PMML Structure
17(18)
2.1 Header
18(1)
2.2 Data Dictionary
19(1)
2.2.1 Categorical Entries
19(1)
2.2.2 Continuous Entries
20(1)
2.3 Data Transformations
21(1)
2.3.1 Transformation Dictionary
22(1)
2.3.2 Local Transformations
23(1)
2.4 Model
23(2)
2.5 Mining Schema
25(3)
2.6 Targets
28(3)
2.7 Output
31(1)
2.7.1 OutputField
31(4)
II Data Manipulation
35(30)
3 Transformations
37(14)
3.1 Continuous Normalization
37(1)
3.2 Discrete Normalization
38(2)
3.3 Discretization
40(4)
3.4 Value Mapping
44(7)
4 Functions
51(14)
4.1 Built-in Functions
51(10)
4.2 DefineFunction
61(4)
III Modeling Techniques
65(82)
5 Association Rules
67(10)
5.1 Association Rules in PMML
67(2)
5.1.1 Item
69(1)
5.1.2 Itemset
70(1)
5.1.3 AssociationRule
71(2)
5.2 Output
73(4)
6 Clustering Models
77(10)
6.1 Clustering Models in PMML
77(1)
6.1.1 Distance and Similarity Measures
78(3)
6.1.2 Clusters
81(1)
6.2 TwoStep Clustering Model
82(1)
6.2.1 ModelStats
83(1)
6.2.2 Partition and Covariance
84(3)
7 Decision Trees
87(6)
7.1 Decision Trees in PMML
87(6)
8 Naive Bayes
93(4)
8.1 Naive Bayes in PMML
93(1)
8.1.1 BayesInput
94(2)
8.1.2 BayesOutput
96(1)
9 Neural Network Models
97(12)
9.1 Back-Propagation Network
97(1)
9.1.1 Back-Propagation Network in PMML
98(5)
9.2 Radial-Basis Network
103(1)
9.2.1 Radial-Basis Network in PMML
104(2)
9.3 Topology Representing Network
106(1)
9.3.1 TRN in PMML
107(2)
10 Regression Models
109(4)
10.1 Regression Functions
109(1)
10.2 Regression Models in PMML
110(3)
11 General Regression Models
113(6)
12 Scorecards
119(8)
12.1 Score Allocation for Categorical Attributes
119(2)
12.2 Score Allocation for Continuous Attributes
121(2)
12.3 Score Allocation for Complex Attributes
123(2)
12.4 Computing the Overall Score
125(2)
13 Support Vector Machines
127(8)
13.1 SVM in PMML
128(7)
14 Time Series Models
135(12)
14.1 Time Series Models in PMML
135(1)
14.1.1 Data Representation
136(2)
14.1.2 TimeAnchor
138(2)
14.1.2.1 TimeCycle and TimeException
140(3)
14.1.3 Representing the Model
143(4)
IV Model Ensemble
147(6)
15 Multiple Models
149(4)
V Model Verification
153(8)
16 Model Verification
155(6)
16.1 VerificationFields
156(1)
16.2 InlineTable
157(4)
VI Conclusion
161(6)
17 Conclusion
163(4)
Index 167