Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition 1788295862, 9781788295864

Table of contents :
Cover
Copyright
Mapt upsell
Contributors
Table of Contents
Preface
Chapter 1: Introducing Machine Learning
The origins of machine learning
Uses and abuses of machine learning
Machine learning successes
The limits of machine learning
Machine learning ethics
How machines learn
Data storage
Abstraction
Generalization
Evaluation
Machine learning in practice
Types of input data
Types of machine learning algorithms
Matching input data to algorithms
Machine learning with R
Installing R packages
Loading and unloading R packages
Installing RStudio
Summary
Chapter 2: Managing and Understanding Data
R data structures
Vectors
Factors
Lists
Data frames
Matrices and arrays
Managing data with R
Saving, loading, and removing R data structures
Importing and saving data from CSV files
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
Chapter 3: Lazy Learning – Classification Using Nearest Neighbors
Understanding nearest neighbor classification
The k-NN algorithm
Measuring similarity with distance
Choosing an appropriate k
Preparing data for use with k-NN
Why is the k-NN algorithm lazy?
Example – diagnosing breast cancer with the k-NN 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
Chapter 4: Probabilistic Learning – Classification Using Naive Bayes
Understanding Naive Bayes
Basic concepts of Bayesian methods
Understanding probability
Understanding joint probability
Computing conditional probability with Bayes' theorem
The Naive Bayes algorithm
Classification with Naive Bayes
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 – cleaning and standardizing text data
Data preparation – splitting text documents into words
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
Chapter 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 cost more than others
Understanding classification rules
Separate and conquer
The 1R algorithm
The RIPPER algorithm
Rules from decision trees
What makes trees and rules greedy?
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
Chapter 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 nonlinear relationships
Transformation – converting a numeric variable to a binary indicator
Model specification – adding interaction effects
Putting it all together – an improved regression model
Making predictions with a 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 the mean absolute error
Step 5 – improving model performance
Summary
Chapter 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
Example – 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
The case of linearly separable data
The case of nonlinearly separable data
Using kernels for nonlinear spaces
Example – 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
Changing the SVM kernel function
Identifying the best SVM cost parameter
Summary
Chapter 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 the 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
Chapter 9: Finding Groups of Data – Clustering with k-means
Understanding clustering
Clustering as a machine learning task
The k-means clustering algorithm
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 the missing values
Step 3 – training a model on the data
Step 4 – evaluating model performance
Step 5 – improving model performance
Summary
Chapter 10: Evaluating Model Performance
Measuring performance for classification
Understanding a classifier's predictions
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 with ROC curves
Estimating future performance
The holdout method
Cross-validation
Bootstrap sampling
Summary
Chapter 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 in a simulated competition
Summary
Chapter 12: Specialized Machine Learning Topics
Managing and preparing real-world data
Making data "tidy" with the tidyverse packages
Generalizing tabular data structures with tibble
Speeding and simplifying data preparation with dplyr
Reading and writing to external data files
Importing tidy tables with readr
Importing Microsoft Excel, SAS, SPSS, and Stata files with rio
Querying data in SQL databases
The tidy approach to managing database connections
Using a database backend with dplyr
A traditional approach to SQL connectivity with RODBC
Working with online data and services
Downloading the complete text of web pages
Parsing the data within web pages
Parsing XML documents
Parsing JSON from web APIs
Working with domain-specific data
Analyzing bioinformatics data
Analyzing and visualizing network 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 multicore and snow
Taking advantage of parallel with foreach and doParallel
Training and evaluating models in parallel with caret
Parallel cloud computing with MapReduce and Hadoop
Parallel cloud computing with Apache Spark
Deploying optimized learning algorithms
Building bigger regression models with biglm
Growing random forests faster with ranger
Growing massive random forests with bigrf
A faster machine learning computing engine with H2O
GPU computing
Flexible numeric computing and machine learning with TensorFlow
An interface for deep learning with Keras
Summary
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