Machine learning microbiome statistics de xia yinglin (12 resultados)
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Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
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EUR 155,29
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Condición: New.
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
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EUR 158,14
Envío por EUR 2,32Se envía dentro de Estados Unidos de AmericaCantidad disponible: 10 disponibles
Condición: New.
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Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
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EUR 161,55
Envío por EUR 7,53Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 4 disponibles
Condición: New.
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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EUR 158,88
Envío por EUR 9,95Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 4 disponibles
Condición: New.
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Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail
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EUR 177,46
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Hardcover. Condición: new. Hardcover. Machine learning fundamentally learns from the past experiences (seen data) to make predictions about future (unseen data). Predictions in nature are often uncertain. Microbiome data have unique characteristics, including high-dimensionality, over-dispersion, sparsity and zero-inflation, and… heterogeneity. Thus, machine learning involving microbiome data for predicting the outcome of phenotypes is even more uncertain than learning those data from other fields. Machine Learning for Microbiome Statistics poses many challenges for evaluating the prediction performance using appropriate metrics and independent data validation.This unique book aims to address the challenges of machine learning statistics, emphasize the importance of performance valuation by appropriate metrics and independent data, and describe several important concepts of machine learning statistics, such as feature engineering and overfitting. It comprehensively reviews commonly used and newly developed machine learning models for microbiome research. Specifically, this book provides the step-by-step procedures to perform machine learning of microbiome data, including feature engineering, algorithm selection and optimization, performance evaluation and model testing. It comments the benefits and limitations of using machine learning for microbiome statistics and remarks on the advantages and disadvantages of each machine learning algorithm.It will be an excellent reference book for students and academics in the field.Presents a thorough overview of machine learning algorithms for microbiome statistics.Performs step-by-step procedures to perform machine learning of microbiome data, using important supervised learning algorithms, including classical, ensemble learning and tree-based models.Describes important concepts of machine learning, including bias and variance tradeoff, accuracy and precision, overfitting and underfitting, model complexity and interpretability, and feature engineering.Investigates and applies various cross-validation techniques step-by-step.Introduces confusion matrix and its derived measures. Comprehensively describes the properties of F1, Matthews correlation coefficient (MCC), area under the receiver operating characteristic curve (AUC-ROC), and area under the precision-recall curve (AUC-PR), as well as discusses their advantages and disadvantages when using them for microbiome data.Offers all related R codes and the datasets from the authors first-hand microbiome research and publicly available data. This unique book aims to address the challenges of machine learning statistics, emphasize the importance of performance valuation by appropriate metrics and independent data, and describe several important concepts of machine learning statistics, such as feature engineering and overfitting. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
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EUR 183,58
Envío por EUR 2,32Se envía dentro de Estados Unidos de AmericaCantidad disponible: 10 disponibles
Condición: As New. Unread book in perfect condition.
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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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EUR 170,22
Envío por EUR 17,38Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 10 disponibles
Condición: New.
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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
Contactar con el vendedorVendedor de 5 estrellasCondición: Usado - Como Nuevo
EUR 183,53
Envío por EUR 17,38Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 10 disponibles
Condición: As New. Unread book in perfect condition.
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Librería: CitiRetail, Stevenage, Reino UnidoCitiRetail
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EUR 173,64
Envío por EUR 42,87Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Hardcover. Condición: new. Hardcover. Machine learning fundamentally learns from the past experiences (seen data) to make predictions about future (unseen data). Predictions in nature are often uncertain. Microbiome data have unique characteristics, including high-dimensionality, over-dispersion, sparsity and zero-inflation, and… heterogeneity. Thus, machine learning involving microbiome data for predicting the outcome of phenotypes is even more uncertain than learning those data from other fields. Machine Learning for Microbiome Statistics poses many challenges for evaluating the prediction performance using appropriate metrics and independent data validation.This unique book aims to address the challenges of machine learning statistics, emphasize the importance of performance valuation by appropriate metrics and independent data, and describe several important concepts of machine learning statistics, such as feature engineering and overfitting. It comprehensively reviews commonly used and newly developed machine learning models for microbiome research. Specifically, this book provides the step-by-step procedures to perform machine learning of microbiome data, including feature engineering, algorithm selection and optimization, performance evaluation and model testing. It comments the benefits and limitations of using machine learning for microbiome statistics and remarks on the advantages and disadvantages of each machine learning algorithm.It will be an excellent reference book for students and academics in the field.Presents a thorough overview of machine learning algorithms for microbiome statistics.Performs step-by-step procedures to perform machine learning of microbiome data, using important supervised learning algorithms, including classical, ensemble learning and tree-based models.Describes important concepts of machine learning, including bias and variance tradeoff, accuracy and precision, overfitting and underfitting, model complexity and interpretability, and feature engineering.Investigates and applies various cross-validation techniques step-by-step.Introduces confusion matrix and its derived measures. Comprehensively describes the properties of F1, Matthews correlation coefficient (MCC), area under the receiver operating characteristic curve (AUC-ROC), and area under the precision-recall curve (AUC-PR), as well as discusses their advantages and disadvantages when using them for microbiome data.Offers all related R codes and the datasets from the authors first-hand microbiome research and publicly available data. This unique book aims to address the challenges of machine learning statistics, emphasize the importance of performance valuation by appropriate metrics and independent data, and describe several important concepts of machine learning statistics, such as feature engineering and overfitting. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Machine Learning for Microbiome Statistics
Yinglin Xia|Jun Sun (Department of Medicine, University of Illinois Chicago, USA)
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Librería: moluna, Greven, Alemaniamoluna
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EUR 181,64
Envío por EUR 48,99Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New. Dr. Yinglin Xia is a Clinical Professor in the Department of Medicine at the University of Illinois Chicago. He has published six books on statistical analysis of microbiome and metabolomics data and more than 180 statistical methodology and research pap.
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Librería: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 218,63
Envío por EUR 32,48Se envía de Australia a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Hardcover. Condición: new. Hardcover. Machine learning fundamentally learns from the past experiences (seen data) to make predictions about future (unseen data). Predictions in nature are often uncertain. Microbiome data have unique characteristics, including high-dimensionality, over-dispersion, sparsity and zero-inflation, and… heterogeneity. Thus, machine learning involving microbiome data for predicting the outcome of phenotypes is even more uncertain than learning those data from other fields. Machine Learning for Microbiome Statistics poses many challenges for evaluating the prediction performance using appropriate metrics and independent data validation.This unique book aims to address the challenges of machine learning statistics, emphasize the importance of performance valuation by appropriate metrics and independent data, and describe several important concepts of machine learning statistics, such as feature engineering and overfitting. It comprehensively reviews commonly used and newly developed machine learning models for microbiome research. Specifically, this book provides the step-by-step procedures to perform machine learning of microbiome data, including feature engineering, algorithm selection and optimization, performance evaluation and model testing. It comments the benefits and limitations of using machine learning for microbiome statistics and remarks on the advantages and disadvantages of each machine learning algorithm.It will be an excellent reference book for students and academics in the field.Presents a thorough overview of machine learning algorithms for microbiome statistics.Performs step-by-step procedures to perform machine learning of microbiome data, using important supervised learning algorithms, including classical, ensemble learning and tree-based models.Describes important concepts of machine learning, including bias and variance tradeoff, accuracy and precision, overfitting and underfitting, model complexity and interpretability, and feature engineering.Investigates and applies various cross-validation techniques step-by-step.Introduces confusion matrix and its derived measures. Comprehensively describes the properties of F1, Matthews correlation coefficient (MCC), area under the receiver operating characteristic curve (AUC-ROC), and area under the precision-recall curve (AUC-PR), as well as discusses their advantages and disadvantages when using them for microbiome data.Offers all related R codes and the datasets from the authors first-hand microbiome research and publicly available data. This unique book aims to address the challenges of machine learning statistics, emphasize the importance of performance valuation by appropriate metrics and independent data, and describe several important concepts of machine learning statistics, such as feature engineering and overfitting. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 226,03
Envío por EUR 67,05Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Buch. Condición: Neu. Neuware - Machine learning fundamentally learns from the past experiences (seen data) to make predictions about future (unseen data). Predictions in nature are often uncertain. Microbiome data have unique characteristics, including high-dimensionality, over-dispersion, sparsity and zero-inflation, and heter…ogeneity. Thus, machine learning involving microbiome data for predicting the outcome of phenotypes is even more uncertain than learning those data from other fields. Machine Learning for Microbiome Statistics poses many challenges for evaluating the prediction performance using appropriate metrics and independent data validation.
