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This book describes the important ideas in a variety of fields such as medicine biology finance and marketingnbspin a common conceptual framework While the approach is statistical the emphasis is on concepts rather than mathematics Many examples are given with a liberal use of colour graphics It isnbspa valuable resource for statisticians and anyone interested in data mining in science or industry The books coverage is broad from supervised learning prediction to unsupervised learning The many topics include neural networks support vector machines classification trees and boostingthe first comprehensive treatment of this topic in any book This major new edition features many topics not covered in the original including graphical models random forests ensemble methods least angle regression amp path algorithms for the lasso nonnegative matrix factorisation and spectral clustering There is also a chapter on methods for wide data p bigger than n including multiple testing and false discovery rates
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Time series data analysis is increasingly important due to the massive production of such data through the internet of things the digitalization of healthcare and the rise of smart cities As continuous monitoring and data collection become more common the need for competent time series analysis with both statistical and machine learning techniques will increaseCovering innovations in time series data analysis and use cases from the real world this practical guide will help you solve the most common data engineering and analysis challengesin time series using both traditional statistical and modern machine learning techniques Author Aileen Nielsen offers an accessible wellrounded introduction to time series in both R and Python that will have data scientists software engineers and researchers up and running quicklyYoull get the guidance you need to confidentlyFind and wrangle time series dataUndertake exploratory time series data analysisStore temporal dataSimulate time series dataGenerate and select features for a time seriesMeasure errorForecast and classify time series with machine or deep learningEvaluate accuracy and performance
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Prediction models are important in various fields including medicine physics meteorology and finance Prediction models will become more relevant in the medical field with the increase in knowledge on potential predictors of outcome eg from genetics Also the number of applications will increase eg with targeted early detection of disease and individualized approaches to diagnostic testing and treatment The current era of evidencebased medicine asks for an individualized approach to medical decisionmaking Evidencebased medicine has a central place for metaanalysis to summarize results from randomized controlled trials similarly prediction models may summarize the effects of predictors to provide individu ized predictions of a diagnostic or prognostic outcome Why Read This Book My motivation for working on this book stems primarily from the fact that the development and applications of prediction models are often suboptimal in medical publications With this book I hope to contribute to better understanding of relevant issues and give practical advice on better modelling strategies than are nowadays widely used Issues include a Better predictive modelling is sometimes easily possible eg a large data set with high quality data is available but all continuous predictors are dich omized which is known to have several disadvantages
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