Machine learning toolbox for social scientists: applied predictive analytics with R
"Toolbox for Social Scientists and Policy Analysts covers predictive methods with complementary statistical "tools" that make it mostly self-contained. The inferential statistics is the traditional framework for most data analytics courses in social science and business fields, especi...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton ; London ; New York
CRC Press, Taylor & Francis Group
2024
|
| Ausgabe: | First edition |
| Schriftenreihe: | A Chapman & Hall book
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| Schlagworte: | |
| Zusammenfassung: | "Toolbox for Social Scientists and Policy Analysts covers predictive methods with complementary statistical "tools" that make it mostly self-contained. The inferential statistics is the traditional framework for most data analytics courses in social science and business fields, especially in Economics and Finance. The new organization that this book offers goes beyond standard machine learning code applications, providing intuitive backgrounds for new predictive methods that social science and business students can follow. The book also adds many other modern statistical tools complementary to predictive methods that cannot be easily found in "econometrics" textbooks: nonparametric methods, data exploration with predictive models, penalized regressions, model selection with sparsity, dimension reduction methods, nonparametric time-series predictions, graphical network analysis, algorithmic optimization methods, classification with imbalanced data, and many others. This book is targeted at students and researchers who have no advanced statistical background, but instead coming from the tradition of "inferential statistics". The modern statistical methods the book provides allows it to be effectively used in teaching in the social science and business fields"-- |
| Beschreibung: | Literaturverzeichnis: Seite 569-578 |
| Beschreibung: | xiii, 586 Seiten Illustrationen, Diagramme |
| ISBN: | 9781032463957 9781032463971 |
Internformat
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| 520 | 3 | |a "Toolbox for Social Scientists and Policy Analysts covers predictive methods with complementary statistical "tools" that make it mostly self-contained. The inferential statistics is the traditional framework for most data analytics courses in social science and business fields, especially in Economics and Finance. The new organization that this book offers goes beyond standard machine learning code applications, providing intuitive backgrounds for new predictive methods that social science and business students can follow. The book also adds many other modern statistical tools complementary to predictive methods that cannot be easily found in "econometrics" textbooks: nonparametric methods, data exploration with predictive models, penalized regressions, model selection with sparsity, dimension reduction methods, nonparametric time-series predictions, graphical network analysis, algorithmic optimization methods, classification with imbalanced data, and many others. This book is targeted at students and researchers who have no advanced statistical background, but instead coming from the tradition of "inferential statistics". The modern statistical methods the book provides allows it to be effectively used in teaching in the social science and business fields"-- | |
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Datensatz im Suchindex
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| dewey-full | 300.72/7 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 300 - Social sciences |
| dewey-raw | 300.72/7 |
| dewey-search | 300.72/7 |
| dewey-sort | 3300.72 17 |
| dewey-tens | 300 - Social sciences |
| discipline | Informatik Soziologie Wirtschaftswissenschaften |
| edition | First edition |
| format | Book |
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| id | DE-604.BV049680591 |
| illustrated | Illustrated |
| indexdate | 2024-07-20T07:55:06Z |
| institution | BVB |
| isbn | 9781032463957 9781032463971 |
| language | English |
| oclc_num | 1406810883 |
| open_access_boolean | |
| owner | DE-188 |
| owner_facet | DE-188 |
| physical | xiii, 586 Seiten Illustrationen, Diagramme |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | CRC Press, Taylor & Francis Group |
| record_format | marc |
| series2 | A Chapman & Hall book |
| spelling | Aydede, Yigit Verfasser aut Machine learning toolbox for social scientists applied predictive analytics with R Yigit Aydede First edition Boca Raton ; London ; New York CRC Press, Taylor & Francis Group 2024 xiii, 586 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier A Chapman & Hall book Literaturverzeichnis: Seite 569-578 "Toolbox for Social Scientists and Policy Analysts covers predictive methods with complementary statistical "tools" that make it mostly self-contained. The inferential statistics is the traditional framework for most data analytics courses in social science and business fields, especially in Economics and Finance. The new organization that this book offers goes beyond standard machine learning code applications, providing intuitive backgrounds for new predictive methods that social science and business students can follow. The book also adds many other modern statistical tools complementary to predictive methods that cannot be easily found in "econometrics" textbooks: nonparametric methods, data exploration with predictive models, penalized regressions, model selection with sparsity, dimension reduction methods, nonparametric time-series predictions, graphical network analysis, algorithmic optimization methods, classification with imbalanced data, and many others. This book is targeted at students and researchers who have no advanced statistical background, but instead coming from the tradition of "inferential statistics". The modern statistical methods the book provides allows it to be effectively used in teaching in the social science and business fields"-- Data Mining (DE-588)4428654-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Social sciences / Statistical methods Parametric modeling Machine learning Data mining R (Computer program language) Maschinelles Lernen (DE-588)4193754-5 s Data Mining (DE-588)4428654-5 s DE-604 Erscheint auch als Online-Ausgabe Aydede, Yigit Machine learning toolbox for social scientists Boca Raton ; London ; New Yor : Press, Taylor & Francis Group,2024 978-1-00-338150-1 |
| spellingShingle | Aydede, Yigit Machine learning toolbox for social scientists applied predictive analytics with R Data Mining (DE-588)4428654-5 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)4193754-5 |
| title | Machine learning toolbox for social scientists applied predictive analytics with R |
| title_auth | Machine learning toolbox for social scientists applied predictive analytics with R |
| title_exact_search | Machine learning toolbox for social scientists applied predictive analytics with R |
| title_full | Machine learning toolbox for social scientists applied predictive analytics with R Yigit Aydede |
| title_fullStr | Machine learning toolbox for social scientists applied predictive analytics with R Yigit Aydede |
| title_full_unstemmed | Machine learning toolbox for social scientists applied predictive analytics with R Yigit Aydede |
| title_short | Machine learning toolbox for social scientists |
| title_sort | machine learning toolbox for social scientists applied predictive analytics with r |
| title_sub | applied predictive analytics with R |
| topic | Data Mining (DE-588)4428654-5 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Data Mining Maschinelles Lernen |
| work_keys_str_mv | AT aydedeyigit machinelearningtoolboxforsocialscientistsappliedpredictiveanalyticswithr |