Feature engineering and selection: a practical approach for predictive models
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton ; London ; New York
CRC Press, Taylor & Francis Group
[2020]
|
| Series: | Data science series
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | xv, 297 Seiten Diagramme 27 cm |
| ISBN: | 9781138079229 9781032090856 |
Staff View
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV047404212 | ||
| 003 | DE-604 | ||
| 005 | 20221125 | ||
| 007 | t| | ||
| 008 | 210806s2020 xxu|||| |||| 00||| eng d | ||
| 020 | |a 9781138079229 |c hardcover |9 978-1-138-07922-9 | ||
| 020 | |a 9781032090856 |c paperback |9 978-1-03-209085-6 | ||
| 035 | |a (OCoLC)1184757145 | ||
| 035 | |a (DE-599)KXP1681714132 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 044 | |a xxu |c XD-US | ||
| 049 | |a DE-1102 |a DE-355 |a DE-473 |a DE-1043 | ||
| 050 | 0 | |a TJ217.6 | |
| 082 | 0 | |a 629.80285/53 | |
| 084 | |a ST 270 |0 (DE-625)143638: |2 rvk | ||
| 084 | |a ST 302 |0 (DE-625)143652: |2 rvk | ||
| 084 | |a ST 601 |0 (DE-625)143682: |2 rvk | ||
| 100 | 1 | |a Kuhn, Max |e Verfasser |0 (DE-588)1093876328 |4 aut | |
| 245 | 1 | 0 | |a Feature engineering and selection |b a practical approach for predictive models |c Max Kuhn, Kjell Johnson |
| 264 | 1 | |a Boca Raton ; London ; New York |b CRC Press, Taylor & Francis Group |c [2020] | |
| 300 | |a xv, 297 Seiten |b Diagramme |c 27 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Data science series | |
| 500 | |a Includes bibliographical references and index | ||
| 650 | 0 | 7 | |a Merkmalsextraktion |0 (DE-588)4314440-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a R |g Programm |0 (DE-588)4705956-4 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Mustererkennung |0 (DE-588)4040936-3 |2 gnd |9 rswk-swf |
| 653 | 0 | |a Predictive control / Data processing | |
| 653 | 0 | |a Predictive control / Mathematical models | |
| 653 | 0 | |a R (Computer program language) | |
| 653 | 0 | |a Predictive control ; Mathematical models | |
| 653 | 0 | |a R (Computer program language) | |
| 689 | 0 | 0 | |a R |g Programm |0 (DE-588)4705956-4 |D s |
| 689 | 0 | 1 | |a Mustererkennung |0 (DE-588)4040936-3 |D s |
| 689 | 0 | 2 | |a Merkmalsextraktion |0 (DE-588)4314440-8 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Johnson, Kjell |e Verfasser |0 (DE-588)109387998X |4 aut | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 9781351609470 |
| 856 | 4 | 2 | |m Digitalisierung UB Regensburg - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032805223&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-032805223 | |
Record in the Search Index
| _version_ | 1822950660870504448 |
|---|---|
| adam_text |
Contents Preface · xi Author Bios · xv 1 Introduction · 1 1.1 1.2 1.3 1.4 1.5 1.6 A Simple Example · 4 Important Concepts · 7 A More Complex Example · 15 Feature Selection · 17 An Outline of the Book · 18 Computing · 20 2 Illustrative Example: Predicting Risk of Ischemic Stroke · 21 2.1 2.2 2.3 2.4 2.5 2.6 Splitting · 23 Preprocessing · 23 Exploration · 26 Predictive Modeling across Sets · 30 Other Considerations · 34 Computing · 34 3 A Review of the Predictive Modeling Process · 35 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10 Illustrative Example: OkCupid Profile Data · 35 Measuring Performance · 36 Data Splitting · 46 Resampling · 47 Tuning Parameters and Overfitting · 56 Model Optimization and Tuning · 57 Comparing Models Using the Training Set · 61 Feature Engineering without Overfitting · 62 Summary · 64 Computing · 64 4 Exploratory Visualizations · 65 4.1 Introduction to the Chicago Train Ridership Data · 66 vii
Contents 4.2 4.3 4.4 4.5 4.6 Visualizations for Numeric Data: Exploring Train Ridership Data · 69 Visualizations for Categorical Data: Exploring the OkCupid Data · 83 Postmodeling Exploratory Visualizations · 88 Summary · 92 Computing · 92 5 Encoding Categorical Predictors · 93 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 Creating Dummy Variables for Unordered Categories · 94 Encoding Predictors with Many Categories · 96 Approaches for Novel Categories · 102 Supervised Encoding Methods · 102 Encodings for Ordered Data · 107 Creating Features from Text Data · 109 Factors versus Dummy Variables in Tree-Based Models · 114 Summary · 119 Computing · 120 6 Engineering Numeric Predictors · 121 6.1 6.2 6.3 6.4 6.5 1:1 Transformations · 122 l:Many Transformations · 126 Many.-Many Transformations · 133 Summary · 154 Computing · 155 7 Detecting Interaction Effects · 157 7.1 7.2 7.3 7.4 7.5 7.6 7.7 Guiding Principles in the Search for Interactions · 161 Practical Considerations · 164 The Brute-Force Approach to Identifying Predictive Interactions · 165 Approaches when Complete Enumeration Is Practically Impossible · 172 Other Potentially Useful Tools · 184 Summary · 185 Computing · 186 8 Handling Missing Data · 187 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 Understanding the Nature and Severity of Missing Information · 189 Models that Are Resistant to Missing Values · 195 Deletion of Data · 196 Encoding Missingness · 197 Imputation Methods · 198 Special Cases · 203 Summary · 203 Computing · 204 9 Working with Profile Data · 205 9.1 9.2 9.3 9.4 9.5 Illustrative Data: Pharmaceutical Manufacturing Monitoring
· 209 What Are the Experimental Unit and the Unit of Prediction? · 210 Reducing Background · 214 Reducing Other Noise · 215 Exploiting Correlation · 217
Contents їх 9.6 Impacts of Data Processing on Modeling · 219 9.7 Summary · 224 9.8 Computing · 225 10 Feature Selection Overview · 227 10.1 10.2 10.3 10.4 10.5 10.6 10.7 Goals of Feature Selection · 227 Classes of Feature Selection Methodologies · 228 Effect of Irrelevant Features · 232 Overfitting to Predictors and External Validation · 235 A Case Study · 238 Next Steps · 240 Computing · 240 11 Greedy Search Methods · 241 11.1 11.2 11.3 11.4 11.5 11.6 Illustrative Data: Predicting Parkinson’s Disease Simple Filters · 241 Recursive Feature Elimination · 248 Stepwise Selection · 252 Summary · 254 Computing · 255 12 Global Search Methods · 257 12.1 12.2 12.3 12.4 12.5 12.6 Naive Bayes Models · 257 Simulated Annealing · 260 Genetic Algorithms · 270 Test Set Results · 280 Summary · 281 Computing · 282 Bibliography · 283 Index · 295 · 241 |
| adam_txt |
Contents Preface · xi Author Bios · xv 1 Introduction · 1 1.1 1.2 1.3 1.4 1.5 1.6 A Simple Example · 4 Important Concepts · 7 A More Complex Example · 15 Feature Selection · 17 An Outline of the Book · 18 Computing · 20 2 Illustrative Example: Predicting Risk of Ischemic Stroke · 21 2.1 2.2 2.3 2.4 2.5 2.6 Splitting · 23 Preprocessing · 23 Exploration · 26 Predictive Modeling across Sets · 30 Other Considerations · 34 Computing · 34 3 A Review of the Predictive Modeling Process · 35 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10 Illustrative Example: OkCupid Profile Data · 35 Measuring Performance · 36 Data Splitting · 46 Resampling · 47 Tuning Parameters and Overfitting · 56 Model Optimization and Tuning · 57 Comparing Models Using the Training Set · 61 Feature Engineering without Overfitting · 62 Summary · 64 Computing · 64 4 Exploratory Visualizations · 65 4.1 Introduction to the Chicago Train Ridership Data · 66 vii
Contents 4.2 4.3 4.4 4.5 4.6 Visualizations for Numeric Data: Exploring Train Ridership Data · 69 Visualizations for Categorical Data: Exploring the OkCupid Data · 83 Postmodeling Exploratory Visualizations · 88 Summary · 92 Computing · 92 5 Encoding Categorical Predictors · 93 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 Creating Dummy Variables for Unordered Categories · 94 Encoding Predictors with Many Categories · 96 Approaches for Novel Categories · 102 Supervised Encoding Methods · 102 Encodings for Ordered Data · 107 Creating Features from Text Data · 109 Factors versus Dummy Variables in Tree-Based Models · 114 Summary · 119 Computing · 120 6 Engineering Numeric Predictors · 121 6.1 6.2 6.3 6.4 6.5 1:1 Transformations · 122 l:Many Transformations · 126 Many.-Many Transformations · 133 Summary · 154 Computing · 155 7 Detecting Interaction Effects · 157 7.1 7.2 7.3 7.4 7.5 7.6 7.7 Guiding Principles in the Search for Interactions · 161 Practical Considerations · 164 The Brute-Force Approach to Identifying Predictive Interactions · 165 Approaches when Complete Enumeration Is Practically Impossible · 172 Other Potentially Useful Tools · 184 Summary · 185 Computing · 186 8 Handling Missing Data · 187 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 Understanding the Nature and Severity of Missing Information · 189 Models that Are Resistant to Missing Values · 195 Deletion of Data · 196 Encoding Missingness · 197 Imputation Methods · 198 Special Cases · 203 Summary · 203 Computing · 204 9 Working with Profile Data · 205 9.1 9.2 9.3 9.4 9.5 Illustrative Data: Pharmaceutical Manufacturing Monitoring
· 209 What Are the Experimental Unit and the Unit of Prediction? · 210 Reducing Background · 214 Reducing Other Noise · 215 Exploiting Correlation · 217
Contents їх 9.6 Impacts of Data Processing on Modeling · 219 9.7 Summary · 224 9.8 Computing · 225 10 Feature Selection Overview · 227 10.1 10.2 10.3 10.4 10.5 10.6 10.7 Goals of Feature Selection · 227 Classes of Feature Selection Methodologies · 228 Effect of Irrelevant Features · 232 Overfitting to Predictors and External Validation · 235 A Case Study · 238 Next Steps · 240 Computing · 240 11 Greedy Search Methods · 241 11.1 11.2 11.3 11.4 11.5 11.6 Illustrative Data: Predicting Parkinson’s Disease Simple Filters · 241 Recursive Feature Elimination · 248 Stepwise Selection · 252 Summary · 254 Computing · 255 12 Global Search Methods · 257 12.1 12.2 12.3 12.4 12.5 12.6 Naive Bayes Models · 257 Simulated Annealing · 260 Genetic Algorithms · 270 Test Set Results · 280 Summary · 281 Computing · 282 Bibliography · 283 Index · 295 · 241 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Kuhn, Max Johnson, Kjell |
| author_GND | (DE-588)1093876328 (DE-588)109387998X |
| author_facet | Kuhn, Max Johnson, Kjell |
| author_role | aut aut |
| author_sort | Kuhn, Max |
| author_variant | m k mk k j kj |
| building | Verbundindex |
| bvnumber | BV047404212 |
| callnumber-first | T - Technology |
| callnumber-label | TJ217 |
| callnumber-raw | TJ217.6 |
| callnumber-search | TJ217.6 |
| callnumber-sort | TJ 3217.6 |
| callnumber-subject | TJ - Mechanical Engineering and Machinery |
| classification_rvk | ST 270 ST 302 ST 601 |
| ctrlnum | (OCoLC)1184757145 (DE-599)KXP1681714132 |
| dewey-full | 629.80285/53 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 629 - Other branches of engineering |
| dewey-raw | 629.80285/53 |
| dewey-search | 629.80285/53 |
| dewey-sort | 3629.80285 253 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Informatik Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
| discipline_str_mv | Informatik Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>00000nam a2200000 c 4500</leader><controlfield tag="001">BV047404212</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20221125</controlfield><controlfield tag="007">t|</controlfield><controlfield tag="008">210806s2020 xxu|||| |||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781138079229</subfield><subfield code="c">hardcover</subfield><subfield code="9">978-1-138-07922-9</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781032090856</subfield><subfield code="c">paperback</subfield><subfield code="9">978-1-03-209085-6</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1184757145</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KXP1681714132</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="a">xxu</subfield><subfield code="c">XD-US</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-1102</subfield><subfield code="a">DE-355</subfield><subfield code="a">DE-473</subfield><subfield code="a">DE-1043</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TJ217.6</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">629.80285/53</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 270</subfield><subfield code="0">(DE-625)143638:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 302</subfield><subfield code="0">(DE-625)143652:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 601</subfield><subfield code="0">(DE-625)143682:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Kuhn, Max</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1093876328</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Feature engineering and selection</subfield><subfield code="b">a practical approach for predictive models</subfield><subfield code="c">Max Kuhn, Kjell Johnson</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Boca Raton ; London ; New York</subfield><subfield code="b">CRC Press, Taylor & Francis Group</subfield><subfield code="c">[2020]</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">xv, 297 Seiten</subfield><subfield code="b">Diagramme</subfield><subfield code="c">27 cm</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Data science series</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Includes bibliographical references and index</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Merkmalsextraktion</subfield><subfield code="0">(DE-588)4314440-8</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">R</subfield><subfield code="g">Programm</subfield><subfield code="0">(DE-588)4705956-4</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Mustererkennung</subfield><subfield code="0">(DE-588)4040936-3</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Predictive control / Data processing</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Predictive control / Mathematical models</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">R (Computer program language)</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Predictive control ; Mathematical models</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">R (Computer program language)</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">R</subfield><subfield code="g">Programm</subfield><subfield code="0">(DE-588)4705956-4</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Mustererkennung</subfield><subfield code="0">(DE-588)4040936-3</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Merkmalsextraktion</subfield><subfield code="0">(DE-588)4314440-8</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Johnson, Kjell</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)109387998X</subfield><subfield code="4">aut</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Online-Ausgabe</subfield><subfield code="z">9781351609470</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">Digitalisierung UB Regensburg - ADAM Catalogue Enrichment</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032805223&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-032805223</subfield></datafield></record></collection> |
| id | DE-604.BV047404212 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T17:53:42Z |
| indexdate | 2025-02-02T13:00:25Z |
| institution | BVB |
| isbn | 9781138079229 9781032090856 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032805223 |
| oclc_num | 1184757145 |
| open_access_boolean | |
| owner | DE-1102 DE-355 DE-BY-UBR DE-473 DE-BY-UBG DE-1043 |
| owner_facet | DE-1102 DE-355 DE-BY-UBR DE-473 DE-BY-UBG DE-1043 |
| physical | xv, 297 Seiten Diagramme 27 cm |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | CRC Press, Taylor & Francis Group |
| record_format | marc |
| series2 | Data science series |
| spelling | Kuhn, Max Verfasser (DE-588)1093876328 aut Feature engineering and selection a practical approach for predictive models Max Kuhn, Kjell Johnson Boca Raton ; London ; New York CRC Press, Taylor & Francis Group [2020] xv, 297 Seiten Diagramme 27 cm txt rdacontent n rdamedia nc rdacarrier Data science series Includes bibliographical references and index Merkmalsextraktion (DE-588)4314440-8 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf Mustererkennung (DE-588)4040936-3 gnd rswk-swf Predictive control / Data processing Predictive control / Mathematical models R (Computer program language) Predictive control ; Mathematical models R Programm (DE-588)4705956-4 s Mustererkennung (DE-588)4040936-3 s Merkmalsextraktion (DE-588)4314440-8 s DE-604 Johnson, Kjell Verfasser (DE-588)109387998X aut Erscheint auch als Online-Ausgabe 9781351609470 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032805223&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Kuhn, Max Johnson, Kjell Feature engineering and selection a practical approach for predictive models Merkmalsextraktion (DE-588)4314440-8 gnd R Programm (DE-588)4705956-4 gnd Mustererkennung (DE-588)4040936-3 gnd |
| subject_GND | (DE-588)4314440-8 (DE-588)4705956-4 (DE-588)4040936-3 |
| title | Feature engineering and selection a practical approach for predictive models |
| title_auth | Feature engineering and selection a practical approach for predictive models |
| title_exact_search | Feature engineering and selection a practical approach for predictive models |
| title_exact_search_txtP | Feature engineering and selection a practical approach for predictive models |
| title_full | Feature engineering and selection a practical approach for predictive models Max Kuhn, Kjell Johnson |
| title_fullStr | Feature engineering and selection a practical approach for predictive models Max Kuhn, Kjell Johnson |
| title_full_unstemmed | Feature engineering and selection a practical approach for predictive models Max Kuhn, Kjell Johnson |
| title_short | Feature engineering and selection |
| title_sort | feature engineering and selection a practical approach for predictive models |
| title_sub | a practical approach for predictive models |
| topic | Merkmalsextraktion (DE-588)4314440-8 gnd R Programm (DE-588)4705956-4 gnd Mustererkennung (DE-588)4040936-3 gnd |
| topic_facet | Merkmalsextraktion R Programm Mustererkennung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032805223&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT kuhnmax featureengineeringandselectionapracticalapproachforpredictivemodels AT johnsonkjell featureengineeringandselectionapracticalapproachforpredictivemodels |