Machine Learning with R Second Edition by problems, Second Edition 25e2 2580 25a2 Expert techniques for predictive modeling to solve all your data analysis -- Read -- Imperial Library of Trantor

Index

Machine Learning with R Second Edition

Table of Contents Machine Learning with R Second Edition 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 Downloading the color images of this book Errata Piracy Questions

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

Summary

2. Managing and Understanding Data

R data structures

Vectors Factors Lists Data frames Matrixes 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

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

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

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 mistakes more costlier 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

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 the 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

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 non-linear 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

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 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

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

Example – 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

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 trade-offs

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 proprietary files and databases

Reading from and writing to Microsoft Excel, SAS, SPSS, and Stata files Querying data in SQL databases

Working with online data and services

Downloading the complete text of web pages Scraping data from 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

Generalizing tabular data structures with dplyr 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 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

← Prev
Back
Next →

← Prev
Back
Next →