Machine Learning with R. R gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required – this book will take you methodically through every stage of applying machine learning Brett Lantz

Spis treści książki

• Machine Learning with R
  • Table of Contents
  • Machine Learning with R
  • Credits
  • About the Author
  • About the Reviewers
  • www.PacktPub.com
    • Support files, eBooks, discount offers and more
      • Why Subscribe?
      • Free Access for Packt account holders
  • Preface
    • What this book covers
    • What you need for this book
    • Who this book is for
    • Conventions
    • Reader feedback
    • Customer support
      • Downloading the example code
      • Errata
      • Piracy
      • Questions
  • 1. Introducing Machine Learning
    • The origins of machine learning
    • Uses and abuses of machine learning
      • Ethical considerations
    • How do machines learn?
      • Abstraction and knowledge representation
      • Generalization
      • Assessing the success of learning
    • Steps to apply machine learning to your data
    • Choosing a machine learning algorithm
      • Thinking about the input data
      • Thinking about types of machine learning algorithms
      • Matching your data to an appropriate algorithm
    • Using R for machine learning
      • Installing and loading R packages
        • Installing an R package
        • Installing a package using the point-and-click interface
        • Loading an R package
    • Summary
  • 2. Managing and Understanding Data
    • R data structures
    • Vectors
    • Factors
      • Lists
      • Data frames
      • Matrixes and arrays
    • Managing data with R
      • Saving and loading R data structures
      • Importing and saving data from CSV files
      • Importing data from SQL databases
    • Exploring and understanding data
      • Exploring the structure of data
      • Exploring numeric variables
        • Measuring the central tendency mean and median
        • Measuring spread quartiles and the five-number summary
        • Visualizing numeric variables boxplots
        • Visualizing numeric variables histograms
        • Understanding numeric data uniform and normal distributions
        • Measuring spread variance and standard deviation
      • Exploring categorical variables
        • Measuring the central tendency the mode
      • Exploring relationships between variables
        • Visualizing relationships scatterplots
        • Examining relationships two-way cross-tabulations
    • Summary
  • 3. Lazy Learning Classification Using Nearest Neighbors
    • Understanding classification using nearest neighbors
      • The kNN algorithm
        • Calculating distance
        • Choosing an appropriate k
        • Preparing data for use with kNN
      • Why is the kNN algorithm lazy?
    • Diagnosing breast cancer with the kNN algorithm
      • Step 1 collecting data
      • Step 2 exploring and preparing the data
        • Transformation normalizing numeric data
        • Data preparation creating training and test datasets
      • Step 3 training a model on the data
      • Step 4 evaluating model performance
      • Step 5 improving model performance
        • Transformation z-score standardization
        • Testing alternative values of k
    • Summary
  • 4. Probabilistic Learning Classification Using Naive Bayes
    • Understanding naive Bayes
      • Basic concepts of Bayesian methods
        • Probability
        • Joint probability
        • Conditional probability with Bayes theorem
      • The naive Bayes algorithm
        • The naive Bayes classification
        • The Laplace estimator
        • Using numeric features with naive Bayes
    • Example filtering mobile phone spam with the naive Bayes algorithm
      • Step 1 collecting data
      • Step 2 exploring and preparing the data
      • Data preparation processing text data for analysis
        • Data preparation creating training and test datasets
        • Visualizing text data word clouds
        • Data preparation creating indicator features for frequent words
      • Step 3 training a model on the data
      • Step 4 evaluating model performance
      • Step 5 improving model performance
    • Summary
  • 5. Divide and Conquer Classification Using Decision Trees and Rules
    • Understanding decision trees
      • Divide and conquer
      • The C5.0 decision tree algorithm
        • Choosing the best split
        • Pruning the decision tree
    • Example identifying risky bank loans using C5.0 decision trees
      • Step 1 collecting data
      • Step 2 exploring and preparing the data
        • Data preparation creating random training and test datasets
      • Step 3 training a model on the data
      • Step 4 evaluating model performance
      • Step 5 improving model performance
        • Boosting the accuracy of decision trees
        • Making some mistakes more costly than others
    • Understanding classification rules
      • Separate and conquer
      • The One Rule algorithm
      • The RIPPER algorithm
      • Rules from decision trees
    • Example identifying poisonous mushrooms with rule learners
      • Step 1 collecting data
      • Step 2 exploring and preparing the data
      • Step 3 training a model on the data
      • Step 4 evaluating model performance
      • Step 5 improving model performance
    • Summary
  • 6. Forecasting Numeric Data Regression Methods
    • Understanding regression
      • Simple linear regression
      • Ordinary least squares estimation
      • Correlations
      • Multiple linear regression
    • Example predicting medical expenses using linear regression
      • Step 1 collecting data
      • Step 2 exploring and preparing the data
        • Exploring relationships among features the correlation matrix
        • Visualizing relationships among features the scatterplot matrix
      • Step 3 training a model on the data
      • Step 4 evaluating model performance
      • Step 5 improving model performance
        • Model specification adding non-linear relationships
        • Transformation converting a numeric variable to a binary indicator
        • Model specification adding interaction effects
        • Putting it all together an improved regression model
    • Understanding regression trees and model trees
      • Adding regression to trees
    • Example estimating the quality of wines with regression trees and model trees
      • Step 1 collecting data
      • Step 2 exploring and preparing the data
      • Step 3 training a model on the data
        • Visualizing decision trees
      • Step 4 evaluating model performance
        • Measuring performance with mean absolute error
      • Step 5 improving model performance
    • Summary
  • 7. Black Box Methods Neural Networks and Support Vector Machines
    • Understanding neural networks
      • From biological to artificial neurons
      • Activation functions
      • Network topology
        • The number of layers
        • The direction of information travel
        • The number of nodes in each layer
      • Training neural networks with backpropagation
    • Modeling the strength of concrete with ANNs
      • Step 1 collecting data
      • Step 2 exploring and preparing the data
      • Step 3 training a model on the data
      • Step 4 evaluating model performance
      • Step 5 improving model performance
    • Understanding Support Vector Machines
      • Classification with hyperplanes
      • Finding the maximum margin
        • The case of linearly separable data
        • The case of non-linearly separable data
      • Using kernels for non-linear spaces
    • Performing OCR with SVMs
      • Step 1 collecting data
      • Step 2 exploring and preparing the data
      • Step 3 training a model on the data
      • Step 4 evaluating model performance
      • Step 5 improving model performance
    • Summary
  • 8. Finding Patterns Market Basket Analysis Using Association Rules
    • Understanding association rules
      • The Apriori algorithm for association rule learning
        • Measuring rule interest support and confidence
        • Building a set of rules with the Apriori principle
    • Example identifying frequently purchased groceries with association rules
      • Step 1 collecting data
      • Step 2 exploring and preparing the data
        • Data preparation creating a sparse matrix for transaction data
        • Visualizing item support item frequency plots
        • Visualizing transaction data plotting the sparse matrix
      • Step 3 training a model on the data
      • Step 4 evaluating model performance
      • Step 5 improving model performance
        • Sorting the set of association rules
        • Taking subsets of association rules
        • Saving association rules to a file or data frame
    • Summary
  • 9. Finding Groups of Data Clustering with k-means
    • Understanding clustering
      • Clustering as a machine learning task
      • The k-means algorithm for clustering
        • Using distance to assign and update clusters
        • Choosing the appropriate number of clusters
      • Finding teen market segments using k-means clustering
      • Step 1 collecting data
      • Step 2 exploring and preparing the data
        • Data preparation dummy coding missing values
        • Data preparation imputing missing values
      • Step 3 training a model on the data
      • Step 4 evaluating model performance
      • Step 5 improving model performance
    • Summary
  • 10. Evaluating Model Performance
    • Measuring performance for classification
      • Working with classification prediction data in R
      • A closer look at confusion matrices
      • Using confusion matrices to measure performance
      • Beyond accuracy other measures of performance
        • The kappa statistic
        • Sensitivity and specificity
        • Precision and recall
        • The F-measure
      • Visualizing performance tradeoffs
        • ROC curves
    • Estimating future performance
      • The holdout method
      • Cross-validation
      • Bootstrap sampling
    • Summary
  • 11. Improving Model Performance
    • Tuning stock models for better performance
      • Using caret for automated parameter tuning
        • Creating a simple tuned model
        • Customizing the tuning process
    • Improving model performance with meta-learning
      • Understanding ensembles
      • Bagging
      • Boosting
      • Random forests
        • Training random forests
        • Evaluating random forest performance
    • Summary
  • 12. Specialized Machine Learning Topics
    • Working with specialized data
      • Getting data from the Web with the RCurl package
      • Reading and writing XML with the XML package
      • Reading and writing JSON with the rjson package
      • Reading and writing Microsoft Excel spreadsheets using xlsx
      • Working with bioinformatics data
      • Working with social network data and graph data
    • Improving the performance of R
      • Managing very large datasets
        • Making data frames faster with data.table
        • Creating disk-based data frames with ff
        • Using massive matrices with bigmemory
      • Learning faster with parallel computing
        • Measuring execution time
        • Working in parallel with foreach
        • Using a multitasking operating system with multicore
        • Networking multiple workstations with snow and snowfall
        • Parallel cloud computing with MapReduce and Hadoop
      • GPU computing
      • Deploying optimized learning algorithms
        • Building bigger regression models with biglm
        • Growing bigger and faster random forests with bigrf
        • Training and evaluating models in parallel with caret
    • Summary
  • Index

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