Mastering Predictive Analytics with R > PACKT 원서리스트

1: Gearing Up for Predictive Modeling
Models
Types of models
The process of predictive modeling
Performance metrics
Summary

2: Linear Regression
Introduction to linear regression
Simple linear regression
Multiple linear regression
Assessing linear regression models
Problems with linear regression
Feature selection
Regularization
Summary

3: Logistic Regression
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
Summary

4: Neural Networks
The biological neuron
The artificial neuron
Stochastic gradient descent
Multilayer perceptron networks
Predicting the energy efficiency of buildings
Predicting glass type revisited
Predicting handwritten digits
Summary

5: Support Vector Machines
Maximal margin classification
Support vector classification
Kernels and support vector machines
Predicting chemical biodegration
Cross-validation
Predicting credit scores
Multiclass classification with support vector machines
Summary

6: 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
Summary

7: Ensemble Methods
Bagging
Boosting
Predicting atmospheric gamma ray radiation
Predicting complex skill learning with boosting
Random forests
Summary

8: Probabilistic Graphical Models
A little graph theory
Bayes' Theorem
Conditional independence
Bayesian networks
The Naïve Bayes classifier
Hidden Markov models
Predicting promoter gene sequences
Predicting letter patterns in English words
Summary

9: Time Series Analysis
Fundamental concepts of time series
Some fundamental time series
Stationarity
Stationary time series models
Non-stationary time series models
Predicting intense earthquakes
Predicting lynx trappings
Predicting foreign exchange rates
Other time series models
Summary

10: Topic Modeling
An overview of topic modeling
Latent Dirichlet Allocation
Modeling the topics of online news stories
Summary

11: Recommendation Systems
Rating matrix
Collaborative filtering
Singular value decomposition
R and Big Data
Predicting recommendations for movies and jokes
Loading and preprocessing the data
Exploring the data
Other approaches to recommendation systems
Summary

backindex: Appendix A: Index