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Mastering predictive analytics with R : master the craft of predictive modeling by developing strategy, intuition, and a solid foundation in essential concepts /
This book is intended for the budding data scientist, predictive modeler, or quantitative analyst with only a basic exposure to R and statistics. It is also designed to be a reference for experienced professionals wanting to brush up on the details of a particular type of predictive model. Mastering...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2015.
|
| Colección: | Community experience distilled.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Forte, Rui Miguel, |e author. | |
| 245 | 1 | 0 | |a Mastering predictive analytics with R : |b master the craft of predictive modeling by developing strategy, intuition, and a solid foundation in essential concepts / |c Rui Miguel Forte. |
| 246 | 3 | 0 | |a Master the craft of predictive modeling by developing strategy, intuition, and a solid foundation in essential concepts |
| 264 | 1 | |a Birmingham, UK : |b Packt Publishing, |c 2015. | |
| 300 | |a 1 online resource (1 volume) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Community experience distilled | |
| 588 | 0 | |a Online resource; title from cover (Safari, viewed July 22, 2015). | |
| 500 | |a Includes index. | ||
| 505 | 0 | |a Cover -- Copyright -- Credits -- About the Author -- Acknowledgments -- About the Reviewers -- www.PacktPub.com -- 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 models -- 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 data -- Exploratory 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 -- Performance metrics -- Assessing regression models -- Assessing classification models -- Assessing binary classification models -- Summary -- Chapter 2 : Linear Regression -- 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 -- Multicollinearity -- Outliers -- Feature selection -- Regularization -- Ridge regression -- Least absolute shrinkage and selection operator (lasso) -- Implementing regularization in R -- Summary -- Chapter 3 : Logistic Regression. | |
| 505 | 8 | |a Classifying with linear regression -- 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 metrics -- Extensions of the binary logistic classifier -- Multinomial logistic regression -- Predicting glass type -- Ordinal logistic regression -- Predicting wine quality -- Summary -- Chapter 4 : 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 -- Predicting the energy efficiency of buildings -- Evaluating multilayer perceptrons for regression -- Predicting glass type revisited -- Predicting handwritten digits -- Receiver operating characteristic curves -- Summary -- Chapter 5 : Support Vector Machines -- Maximal margin classification -- Support vector classification -- Inner products -- Kernels and support vector machines -- Predicting chemical biodegration -- Cross-validation -- Predicting credit scores -- Multi-class classification with support vector machines -- Summary -- Chapter 6 : Tree-based Methods -- The intuition for tree models -- Algorithms for training decision trees -- Classification and regression trees -- CART regression trees -- Tree pruning -- Missing data -- Regression model trees -- CART classification trees -- C5.0 -- Predicting class membership on synthetic 2D data -- Predicting the authenticity of banknotes -- Predicting complex skill learning -- Tuning model parameters in CART trees -- Variable importance in tree models. | |
| 505 | 8 | |a Regression model trees in action -- Summary -- Chapter 7 : Ensemble Methods -- Bagging -- Margins and out-of-bag observations -- Predicting complex skill learning with bagging -- Predicting heart disease with bagging -- Limitations of bagging -- Boosting -- AdaBoost -- Predicting atmospheric gamma ray radiation -- Predicting complex skill learning with boosting -- Limitations of boosting -- Random forests -- The importance of variables in random forests -- Summary -- Chapter 8 : Probabilistic Graphical Models -- A Little Graph Theory -- Bayes' Theorem -- Conditional independence -- Bayesian networks -- The Naïve Bayes classifier -- Predicting the sentiment of movie reviews -- Hidden Markov models -- Predicting promoter gene sequences -- Predicting letter patterns in English words -- Summary -- Chapter 9 : Time Series Analysis -- Fundamental concepts of time series -- Time series summary functions -- Some fundamental time series -- White noise -- Fitting a white noise time series -- Random walk -- Fitting a random walk -- Stationarity -- Stationary time series models -- Moving average models -- Autoregressive models -- Autoregressive moving average models -- Non-stationary time series models -- Autoregressive integrated moving average models -- Autoregressive conditional heteroscedasticity models -- Generalized autoregressive heteroscedasticity models -- Predicting intense earthquakes -- Predicting lynx trappings -- Predicting foreign exchange rates -- Other time series models -- Summary -- Chapter 10 : Topic Modeling -- An overview of topic modeling -- Latent Dirichlet Allocation -- The Dirichlet distribution -- The generative process -- Fitting an LDA model -- Modeling the topics of online news stories -- Model stability -- Finding the number of topics -- Topic distributions -- Word distributions -- LDA extensions -- Summary. | |
| 505 | 8 | |a Chapter 11 : Recommendation Systems -- Rating matrix -- Measuring user similarity -- Collaborative filtering -- User-based collaborative filtering -- Item-based collaborative filtering -- Singular value decomposition -- R and Big Data -- Predicting recommendations for movies and jokes -- Loading and preprocessing the data -- Exploring the data -- Evaluating binary top-N recommendations -- Evaluating non-binary top-N recommendations -- Evaluating individual predictions -- Other approaches to recommendation systems -- Summary -- Index. | |
| 520 | |a This book is intended for the budding data scientist, predictive modeler, or quantitative analyst with only a basic exposure to R and statistics. It is also designed to be a reference for experienced professionals wanting to brush up on the details of a particular type of predictive model. Mastering Predictive Analytics with R assumes familiarity with only the fundamentals of R, such as the main data types, simple functions, and how to move data around. No prior experience with machine learning or predictive modeling is assumed, however you should have a basic understanding of statistics and calculus at a high school level. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a R (Computer program language) | |
| 650 | 0 | |a Data mining |x Data processing. | |
| 650 | 6 | |a R (Langage de programmation) | |
| 650 | 6 | |a Exploration de données (Informatique) |x Informatique. | |
| 650 | 7 | |a MATHEMATICS |x Applied. |2 bisacsh | |
| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x General. |2 bisacsh | |
| 650 | 7 | |a R (Computer program language) |2 fast | |
| 776 | 0 | 8 | |i Print version: |a Forte, Rui Miguel. |t Mastering predictive analytics with R : master the craft of predictive modeling by developing strategy, intuition, and a solid foundation in essential concepts. |d Birmingham, England ; Mumbai, [India] : Packt Publishing, ©2015 |h xvi, 382 pages |k Community experience distilled. |z 9781783982806 |
| 830 | 0 | |a Community experience distilled. | |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781783982806/?ar |z Texto completo (Requiere registro previo con correo institucional) |
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| 938 | |a ProQuest MyiLibrary Digital eBook Collection |b IDEB |n cis31825873 | ||
| 938 | |a YBP Library Services |b YANK |n 12480625 | ||
| 994 | |a 92 |b IZTAP | ||