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Mastering Predictive Analytics with R（Second Edition）
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Index
Althoughbuddingdatascientists,predictivemodelers,orquantitativeanalystswithonlybasicexposuretoRandstatisticswillfindthisbooktobeuseful,theexperienceddatascientistprofessionalwishingtoattainmasterlevelstatus,willalsofindthisbookextremelyvaluable..ThisbookassumesfamiliaritywiththefundamentalsofR,suchasthemaindatatypes,simplefunctions,andhowtomovedataaround.Althoughnopriorexperiencewithmachinelearningorpredictivemodelingisrequired,therearesomeadvancedtopicsprovidedthatwillrequiremorethannoviceexposure.
目錄(119章)
倒序
- 封面
- 書名頁
- Mastering Predictive Analytics with R Second Edition
- Credits
- About the Authors
- About the Reviewer
- www.PacktPub.com
- Customer Feedback
- Preface
- What this book covers
- What you need for this book
- Who this book is for
- Conventions
- Reader feedback
- Customer support
- Chapter 1. Gearing Up for Predictive Modeling
- Models
- Types of model
- The process of predictive modeling
- Summary
- Chapter 2. Tidying Data and Measuring Performance
- Getting started
- Tidying data
- Categorizing data quality
- Performance metrics
- Cross-validation
- Learning curves
- Summary
- Chapter 3. Linear Regression
- Introduction to linear regression
- Simple linear regression
- Multiple linear regression
- Assessing linear regression models
- Problems with linear regression
- Feature selection
- Regularization
- Polynomial regression
- Summary
- Chapter 4. Generalized Linear Models
- Classifying with linear regression
- Introduction to logistic regression
- Predicting heart disease
- Assessing logistic regression models
- Regularization with the lasso
- Classification metrics
- Extensions of the binary logistic classifier
- Poisson regression
- Negative Binomial regression
- Summary
- Chapter 5. Neural Networks
- The biological neuron
- The artificial neuron
- Stochastic gradient descent
- Multilayer perceptron networks
- The back propagation algorithm
- Predicting the energy efficiency of buildings
- Predicting glass type revisited
- Predicting handwritten digits
- Radial basis function networks
- Summary
- Chapter 6. Support Vector Machines
- Maximal margin classification
- Support vector classification
- Kernels and support vector machines
- Predicting chemical biodegration
- Predicting credit scores
- Multiclass classification with support vector machines
- Summary
- Chapter 7. Tree-Based Methods
- The intuition for tree models
- Algorithms for training decision trees
- Predicting class membership on synthetic 2D data
- Predicting the authenticity of banknotes
- Predicting complex skill learning
- Improvements to the M5 model
- Summary
- Chapter 8. Dimensionality Reduction
- Defining DR
- Summary
- Chapter 9. Ensemble Methods
- Bagging
- Boosting
- Predicting atmospheric gamma ray radiation
- Predicting complex skill learning with boosting
- Summary
- Chapter 10. Probabilistic Graphical Models
- A little graph theory
- Bayes' theorem
- Conditional independence
- Bayesian networks
- The Na?ve Bayes classifier
- Summary
- Chapter 11. Topic Modeling
- An overview of topic modeling
- Latent Dirichlet Allocation
- Modeling the topics of online news stories
- Modeling tweet topics
- Summary
- Chapter 12. Recommendation Systems
- Rating matrix
- Collaborative filtering
- Singular value decomposition
- Predicting recommendations for movies and jokes
- Loading and pre-processing the data
- Exploring the data
- Other approaches to recommendation systems
- Summary
- Chapter 13. Scaling Up
- Starting the project
- Characteristics of big data
- Training models at scale
- A path forward
- Alternatives
- Summary
- Chapter 14. Deep Learning
- Machine learning or deep learning
- What is deep learning?
- Summary
- Index 更新時間：2021-07-02 20:25:42
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