Machine Learning Toolbox Social by Aydede Yigit (24 results)

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HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

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Hardback. Condition: New. Machine Learning Toolbox for Social Scientists 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.Key Features: The book is structured for those who have been trained in a traditional statistics curriculum. There is one long initial section that covers the differences in "estimation" and "prediction" for people trained for causal analysis. The book develops a background framework for Machine learning applications from Nonparametric methods. SVM and NN simple enough without too much detail. It's self-sufficient. Nonparametric time-series predictions are new and covered in a separate section. Additional sections are added: Penalized Regressions, Dimension Reduction Methods, and Graphical Methods have been increasing in their popularity in social sciences. …

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Condition: New. 2023. 1st Edition. hardcover. . . . . .

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Condition: New. 2023. 1st Edition. hardcover. . . . . . Books ship from the US and Ireland.

Hardback. Condition: New. Machine Learning Toolbox for Social Scientists 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.Key Features: The book is structured for those who have been trained in a traditional statistics curriculum. There is one long initial section that covers the differences in "estimation" and "prediction" for people trained for causal analysis. The book develops a background framework for Machine learning applications from Nonparametric methods. SVM and NN simple enough without too much detail. It's self-sufficient. Nonparametric time-series predictions are new and covered in a separate section. Additional sections are added: Penalized Regressions, Dimension Reduction Methods, and Graphical Methods have been increasing in their popularity in social sciences. …

Condition: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.…

Hardcover. Condition: Brand New. 336 pages. 10.00x7.00x1.26 inches. In Stock.

Hardcover. Condition: Brand New. 336 pages. 10.00x7.00x1.26 inches. In Stock. This item is printed on demand.

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Buch. Condition: Neu. Machine Learning Toolbox for Social Scientists | Applied Predictive Analytics with R | Yigit Aydede | Buch | Einband - fest (Hardcover) | Englisch | 2023 | Chapman and Hall/CRC | EAN 9781032463957 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…

Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Machine Learning Toolbox for Social Scientists 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.…