Mastering predictive analytics with R :: machine learning techniques for advanced models /
Master the craft of predictive modeling in R by developing strategy, intuition, and a solid foundation in essential concepts About This Book Grasping the major methods of predictive modeling and moving beyond black box thinking to a deeper level of understanding Leveraging the flexibility and modula...
Gespeichert in:
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing,
2017.
|
| Ausgabe: | 2nd ed. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Master the craft of predictive modeling in R by developing strategy, intuition, and a solid foundation in essential concepts About This Book Grasping the major methods of predictive modeling and moving beyond black box thinking to a deeper level of understanding Leveraging the flexibility and modularity of R to experiment with a range of different techniques and data types Packed with practical advice and tips explaining important concepts and best practices to help you understand quickly and easily Who This Book Is For Although budding data scientists, predictive modelers, or quantitative analysts with only basic exposure to R and statistics will find this book to be useful, the experienced data scientist professional wishing to attain master level status, will also find this book extremely valuable.. This book assumes familiarity with the fundamentals of R, such as the main data types, simple functions, and how to move data around. Although no prior experience with machine learning or predictive modeling is required, there are some advanced topics provided that will require more than novice exposure. What You Will Learn Master the steps involved in the predictive modeling process Grow your expertise in using R and its diverse range of packages Learn how to classify predictive models and distinguish which models are suitable for a particular problem Understand steps for tidying data and improving the performing metrics Recognize the assumptions, strengths, and weaknesses of a predictive model Understand how and why each predictive model works in R Select appropriate metrics to assess the performance of different types of predictive model Explore word embedding and recurrent neural networks in R Train models in R that can work on very large datasets In Detail R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions. With its constantly growing community and plethora of packages, R offers the functionality to deal with a truly vast array of problems. The book begins with a dedicated chapter on the language of models and the predictive modeling process. You will understand the learning curve and the process of tidying data. Each subsequent chapter tackles a particular type of model, such as neural networks, and focuses on the three important questions of how the model works, how to use R to train it, and how to measure and assess its performance using real-world datasets. How do y ... |
| Beschreibung: | Evaluating multilayer perceptrons for regression. |
| Beschreibung: | 1 online resource (449 pages) |
| ISBN: | 9781787124356 1787124355 |
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| 245 | 1 | 0 | |a Mastering predictive analytics with R : |b machine learning techniques for advanced models / |c James D. Miller, Rui Miguel Forte. |
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| 505 | 0 | |a Cover ; Copyright ; Credits; About the Authors; About the Reviewer; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Gearing Up for Predictive Modeling ; Models; Learning from data; The core components of a model; Our first model -- k-nearest neighbors; Types of model; Supervised, unsupervised, semi-supervised, and reinforcement learning models; Parametric and nonparametric models; Regression and classification models; Real-time and batch machine learning models; The process of predictive modeling; Defining the model's objective; Collecting the data; Picking a model. | |
| 505 | 8 | |a Pre-processing the dataExploratory data analysis; Feature transformations; Encoding categorical features; Missing data; Outliers; Removing problematic features; Feature engineering and dimensionality reduction; Training and assessing the model; Repeating with different models and final model selection; Deploying the model; Summary; Chapter 2: Tidying Data and Measuring Performance ; Getting started; Tidying data; Categorizing data quality; The first step; The next step; The final step; Performance metrics; Assessing regression models; Assessing classification models. | |
| 505 | 8 | |a Assessing binary classification modelsCross-validation; Learning curves; Plot and ping; Summary; Chapter 3: Linear Regression ; Introduction to linear regression; Assumptions of linear regression; Simple linear regression; Estimating the regression coefficients; Multiple linear regression; Predicting CPU performance; Predicting the price of used cars; Assessing linear regression models; Residual analysis; Significance tests for linear regression; Performance metrics for linear regression; Comparing different regression models; Test set performance; Problems with linear regression. | |
| 505 | 8 | |a MulticollinearityOutliers; Feature selection; Regularization; Ridge regression; Least absolute shrinkage and selection operator (lasso); Implementing regularization in R; Polynomial regression; Summary; Chapter 4: Generalized Linear Models ; Classifying with linear regression; Introduction to logistic regression; Generalized linear models; Interpreting coefficients in logistic regression; Assumptions of logistic regression; Maximum likelihood estimation; Predicting heart disease; Assessing logistic regression models; Model deviance; Test set performance; Regularization with the lasso. | |
| 505 | 8 | |a Classification metricsExtensions of the binary logistic classifier; Multinomial logistic regression; Predicting glass type; Ordinal logistic regression; Predicting wine quality; Poisson regression; Negative Binomial regression; Summary; Chapter 5: Neural Networks ; The biological neuron; The artificial neuron; Stochastic gradient descent; Gradient descent and local minima; The perceptron algorithm; Linear separation; The logistic neuron; Multilayer perceptron networks; Training multilayer perceptron networks; The back propagation algorithm; Predicting the energy efficiency of buildings. | |
| 500 | |a Evaluating multilayer perceptrons for regression. | ||
| 520 | |a Master the craft of predictive modeling in R by developing strategy, intuition, and a solid foundation in essential concepts About This Book Grasping the major methods of predictive modeling and moving beyond black box thinking to a deeper level of understanding Leveraging the flexibility and modularity of R to experiment with a range of different techniques and data types Packed with practical advice and tips explaining important concepts and best practices to help you understand quickly and easily Who This Book Is For Although budding data scientists, predictive modelers, or quantitative analysts with only basic exposure to R and statistics will find this book to be useful, the experienced data scientist professional wishing to attain master level status, will also find this book extremely valuable.. This book assumes familiarity with the fundamentals of R, such as the main data types, simple functions, and how to move data around. Although no prior experience with machine learning or predictive modeling is required, there are some advanced topics provided that will require more than novice exposure. What You Will Learn Master the steps involved in the predictive modeling process Grow your expertise in using R and its diverse range of packages Learn how to classify predictive models and distinguish which models are suitable for a particular problem Understand steps for tidying data and improving the performing metrics Recognize the assumptions, strengths, and weaknesses of a predictive model Understand how and why each predictive model works in R Select appropriate metrics to assess the performance of different types of predictive model Explore word embedding and recurrent neural networks in R Train models in R that can work on very large datasets In Detail R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions. With its constantly growing community and plethora of packages, R offers the functionality to deal with a truly vast array of problems. The book begins with a dedicated chapter on the language of models and the predictive modeling process. You will understand the learning curve and the process of tidying data. Each subsequent chapter tackles a particular type of model, such as neural networks, and focuses on the three important questions of how the model works, how to use R to train it, and how to measure and assess its performance using real-world datasets. How do y ... | ||
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| contents | Cover ; Copyright ; Credits; About the Authors; About the Reviewer; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Gearing Up for Predictive Modeling ; Models; Learning from data; The core components of a model; Our first model -- k-nearest neighbors; Types of model; Supervised, unsupervised, semi-supervised, and reinforcement learning models; Parametric and nonparametric models; Regression and classification models; Real-time and batch machine learning models; The process of predictive modeling; Defining the model's objective; Collecting the data; Picking a model. Pre-processing the dataExploratory data analysis; Feature transformations; Encoding categorical features; Missing data; Outliers; Removing problematic features; Feature engineering and dimensionality reduction; Training and assessing the model; Repeating with different models and final model selection; Deploying the model; Summary; Chapter 2: Tidying Data and Measuring Performance ; Getting started; Tidying data; Categorizing data quality; The first step; The next step; The final step; Performance metrics; Assessing regression models; Assessing classification models. Assessing binary classification modelsCross-validation; Learning curves; Plot and ping; Summary; Chapter 3: Linear Regression ; Introduction to linear regression; Assumptions of linear regression; Simple linear regression; Estimating the regression coefficients; Multiple linear regression; Predicting CPU performance; Predicting the price of used cars; Assessing linear regression models; Residual analysis; Significance tests for linear regression; Performance metrics for linear regression; Comparing different regression models; Test set performance; Problems with linear regression. MulticollinearityOutliers; Feature selection; Regularization; Ridge regression; Least absolute shrinkage and selection operator (lasso); Implementing regularization in R; Polynomial regression; Summary; Chapter 4: Generalized Linear Models ; Classifying with linear regression; Introduction to logistic regression; Generalized linear models; Interpreting coefficients in logistic regression; Assumptions of logistic regression; Maximum likelihood estimation; Predicting heart disease; Assessing logistic regression models; Model deviance; Test set performance; Regularization with the lasso. Classification metricsExtensions of the binary logistic classifier; Multinomial logistic regression; Predicting glass type; Ordinal logistic regression; Predicting wine quality; Poisson regression; Negative Binomial regression; Summary; Chapter 5: Neural Networks ; The biological neuron; The artificial neuron; Stochastic gradient descent; Gradient descent and local minima; The perceptron algorithm; Linear separation; The logistic neuron; Multilayer perceptron networks; Training multilayer perceptron networks; The back propagation algorithm; Predicting the energy efficiency of buildings. |
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| discipline | Informatik |
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| spelling | Miller, James D. Mastering predictive analytics with R : machine learning techniques for advanced models / James D. Miller, Rui Miguel Forte. 2nd ed. Birmingham : Packt Publishing, 2017. 1 online resource (449 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Print version record. Cover ; Copyright ; Credits; About the Authors; About the Reviewer; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Gearing Up for Predictive Modeling ; Models; Learning from data; The core components of a model; Our first model -- k-nearest neighbors; Types of model; Supervised, unsupervised, semi-supervised, and reinforcement learning models; Parametric and nonparametric models; Regression and classification models; Real-time and batch machine learning models; The process of predictive modeling; Defining the model's objective; Collecting the data; Picking a model. Pre-processing the dataExploratory data analysis; Feature transformations; Encoding categorical features; Missing data; Outliers; Removing problematic features; Feature engineering and dimensionality reduction; Training and assessing the model; Repeating with different models and final model selection; Deploying the model; Summary; Chapter 2: Tidying Data and Measuring Performance ; Getting started; Tidying data; Categorizing data quality; The first step; The next step; The final step; Performance metrics; Assessing regression models; Assessing classification models. Assessing binary classification modelsCross-validation; Learning curves; Plot and ping; Summary; Chapter 3: Linear Regression ; Introduction to linear regression; Assumptions of linear regression; Simple linear regression; Estimating the regression coefficients; Multiple linear regression; Predicting CPU performance; Predicting the price of used cars; Assessing linear regression models; Residual analysis; Significance tests for linear regression; Performance metrics for linear regression; Comparing different regression models; Test set performance; Problems with linear regression. MulticollinearityOutliers; Feature selection; Regularization; Ridge regression; Least absolute shrinkage and selection operator (lasso); Implementing regularization in R; Polynomial regression; Summary; Chapter 4: Generalized Linear Models ; Classifying with linear regression; Introduction to logistic regression; Generalized linear models; Interpreting coefficients in logistic regression; Assumptions of logistic regression; Maximum likelihood estimation; Predicting heart disease; Assessing logistic regression models; Model deviance; Test set performance; Regularization with the lasso. Classification metricsExtensions of the binary logistic classifier; Multinomial logistic regression; Predicting glass type; Ordinal logistic regression; Predicting wine quality; Poisson regression; Negative Binomial regression; Summary; Chapter 5: Neural Networks ; The biological neuron; The artificial neuron; Stochastic gradient descent; Gradient descent and local minima; The perceptron algorithm; Linear separation; The logistic neuron; Multilayer perceptron networks; Training multilayer perceptron networks; The back propagation algorithm; Predicting the energy efficiency of buildings. Evaluating multilayer perceptrons for regression. Master the craft of predictive modeling in R by developing strategy, intuition, and a solid foundation in essential concepts About This Book Grasping the major methods of predictive modeling and moving beyond black box thinking to a deeper level of understanding Leveraging the flexibility and modularity of R to experiment with a range of different techniques and data types Packed with practical advice and tips explaining important concepts and best practices to help you understand quickly and easily Who This Book Is For Although budding data scientists, predictive modelers, or quantitative analysts with only basic exposure to R and statistics will find this book to be useful, the experienced data scientist professional wishing to attain master level status, will also find this book extremely valuable.. This book assumes familiarity with the fundamentals of R, such as the main data types, simple functions, and how to move data around. Although no prior experience with machine learning or predictive modeling is required, there are some advanced topics provided that will require more than novice exposure. What You Will Learn Master the steps involved in the predictive modeling process Grow your expertise in using R and its diverse range of packages Learn how to classify predictive models and distinguish which models are suitable for a particular problem Understand steps for tidying data and improving the performing metrics Recognize the assumptions, strengths, and weaknesses of a predictive model Understand how and why each predictive model works in R Select appropriate metrics to assess the performance of different types of predictive model Explore word embedding and recurrent neural networks in R Train models in R that can work on very large datasets In Detail R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions. With its constantly growing community and plethora of packages, R offers the functionality to deal with a truly vast array of problems. The book begins with a dedicated chapter on the language of models and the predictive modeling process. You will understand the learning curve and the process of tidying data. Each subsequent chapter tackles a particular type of model, such as neural networks, and focuses on the three important questions of how the model works, how to use R to train it, and how to measure and assess its performance using real-world datasets. How do y ... R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Predictive control Mathematical models. R (Langage de programmation) Exploration de données (Informatique) Commande prédictive Modèles mathématiques. COMPUTERS General. bisacsh Data mining fast Predictive control Mathematical models fast R (Computer program language) fast Electronic book. Forte, Rui Miguel. has work: Mastering predictive analytics with R (Text) https://id.oclc.org/worldcat/entity/E39PCH7DyyBxmdrr3HYpTCGdkP https://id.oclc.org/worldcat/ontology/hasWork Print version: Miller, James D. Mastering Predictive Analytics with R - Second Edition. Birmingham : Packt Publishing, ©2017 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1579363 Volltext |
| spellingShingle | Miller, James D. Mastering predictive analytics with R : machine learning techniques for advanced models / Cover ; Copyright ; Credits; About the Authors; About the Reviewer; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Gearing Up for Predictive Modeling ; Models; Learning from data; The core components of a model; Our first model -- k-nearest neighbors; Types of model; Supervised, unsupervised, semi-supervised, and reinforcement learning models; Parametric and nonparametric models; Regression and classification models; Real-time and batch machine learning models; The process of predictive modeling; Defining the model's objective; Collecting the data; Picking a model. Pre-processing the dataExploratory data analysis; Feature transformations; Encoding categorical features; Missing data; Outliers; Removing problematic features; Feature engineering and dimensionality reduction; Training and assessing the model; Repeating with different models and final model selection; Deploying the model; Summary; Chapter 2: Tidying Data and Measuring Performance ; Getting started; Tidying data; Categorizing data quality; The first step; The next step; The final step; Performance metrics; Assessing regression models; Assessing classification models. Assessing binary classification modelsCross-validation; Learning curves; Plot and ping; Summary; Chapter 3: Linear Regression ; Introduction to linear regression; Assumptions of linear regression; Simple linear regression; Estimating the regression coefficients; Multiple linear regression; Predicting CPU performance; Predicting the price of used cars; Assessing linear regression models; Residual analysis; Significance tests for linear regression; Performance metrics for linear regression; Comparing different regression models; Test set performance; Problems with linear regression. MulticollinearityOutliers; Feature selection; Regularization; Ridge regression; Least absolute shrinkage and selection operator (lasso); Implementing regularization in R; Polynomial regression; Summary; Chapter 4: Generalized Linear Models ; Classifying with linear regression; Introduction to logistic regression; Generalized linear models; Interpreting coefficients in logistic regression; Assumptions of logistic regression; Maximum likelihood estimation; Predicting heart disease; Assessing logistic regression models; Model deviance; Test set performance; Regularization with the lasso. Classification metricsExtensions of the binary logistic classifier; Multinomial logistic regression; Predicting glass type; Ordinal logistic regression; Predicting wine quality; Poisson regression; Negative Binomial regression; Summary; Chapter 5: Neural Networks ; The biological neuron; The artificial neuron; Stochastic gradient descent; Gradient descent and local minima; The perceptron algorithm; Linear separation; The logistic neuron; Multilayer perceptron networks; Training multilayer perceptron networks; The back propagation algorithm; Predicting the energy efficiency of buildings. R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Predictive control Mathematical models. R (Langage de programmation) Exploration de données (Informatique) Commande prédictive Modèles mathématiques. COMPUTERS General. bisacsh Data mining fast Predictive control Mathematical models fast R (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh2002004407 http://id.loc.gov/authorities/subjects/sh97002073 |
| title | Mastering predictive analytics with R : machine learning techniques for advanced models / |
| title_auth | Mastering predictive analytics with R : machine learning techniques for advanced models / |
| title_exact_search | Mastering predictive analytics with R : machine learning techniques for advanced models / |
| title_full | Mastering predictive analytics with R : machine learning techniques for advanced models / James D. Miller, Rui Miguel Forte. |
| title_fullStr | Mastering predictive analytics with R : machine learning techniques for advanced models / James D. Miller, Rui Miguel Forte. |
| title_full_unstemmed | Mastering predictive analytics with R : machine learning techniques for advanced models / James D. Miller, Rui Miguel Forte. |
| title_short | Mastering predictive analytics with R : |
| title_sort | mastering predictive analytics with r machine learning techniques for advanced models |
| title_sub | machine learning techniques for advanced models / |
| topic | R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Predictive control Mathematical models. R (Langage de programmation) Exploration de données (Informatique) Commande prédictive Modèles mathématiques. COMPUTERS General. bisacsh Data mining fast Predictive control Mathematical models fast R (Computer program language) fast |
| topic_facet | R (Computer program language) Data mining. Predictive control Mathematical models. R (Langage de programmation) Exploration de données (Informatique) Commande prédictive Modèles mathématiques. COMPUTERS General. Data mining Predictive control Mathematical models Electronic book. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1579363 |
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