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.