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Mastering Predictive Analytics with R（Second Edition）

更新時間：2021-07-02 20:25:42

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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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Mastering Predictive Analytics with R（Second Edition）