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Machine Learning in Action---原版.pdf

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    Machine Learning in Action
    brief contents
    contents
    preface
    acknowledgments
    about this book
    Audience
    Top 10 algorithms in data mining
    How the book is organized
    Part 1 Machine learning basics
    Part 2 Forecasting numeric values with regression
    Part 3 Unsupervised learning
    Part 4 Additional tools
    Examples
    Code conventions and downloads
    Author Online
    about the author
    about the cover illustration
    Classification
    Machine learning basics
    1.1 What is machine learning?
    1.1.1 Sensors and the data deluge
    1.1.2 Machine learning will be more important in the future
    1.2 Key terminology
    1.3 Key tasks of machine learning
    1.4 How to choose the right algorithm
    1.5 Steps in developing a machine learning application
    1.6 Why Python?
    1.6.1 Executable pseudo-code
    1.6.2 Python is popular
    1.6.3 What Python has that other languages don’t have
    1.6.4 Drawbacks
    1.7 Getting started with the NumPy library
    1.8 Summary
    Classifying with k-Nearest Neighbors
    2.1 Classifying with distance measurements
    2.1.1 Prepare: importing data with Python
    2.1.2 Putting the kNN classification algorithm into action
    2.1.3 How to test a classifier
    2.2 Example: improving matches from a dating site with kNN
    2.2.1 Prepare: parsing data from a text file
    2.2.2 Analyze: creating scatter plots with Matplotlib
    2.2.3 Prepare: normalizing numeric values
    2.2.4 Test: testing the classifier as a whole program
    2.2.5 Use: putting together a useful system
    2.3 Example: a handwriting recognition system
    2.3.1 Prepare: converting images into test vectors
    2.3.2 Test: kNN on handwritten digits
    2.4 Summary
    Splitting datasets one feature at a time: decision trees
    3.1 Tree construction
    3.1.1 Information gain
    3.1.2 Splitting the dataset
    3.1.3 Recursively building the tree
    3.2 Plotting trees in Python with Matplotlib annotations
    3.2.1 Matplotlib annotations
    3.2.2 Constructing a tree of annotations
    3.3 Testing and storing the classifier
    3.3.1 Test: using the tree for classification
    3.3.2 Use: persisting the decision tree
    3.4 Example: using decision trees to predict contact lens type
    3.5 Summary
    Classifying with probability theory: naïve Bayes
    4.1 Classifying with Bayesian decision theory
    4.2 Conditional probability
    4.3 Classifying with conditional probabilities
    4.4 Document classification with naïve Bayes
    4.5 Classifying text with Python
    4.5.1 Prepare: making word vectors from text
    4.5.2 Train: calculating probabilities from word vectors
    4.5.3 Test: modifying the classifier for real-world conditions
    4.5.4 Prepare: the bag-of-words document model
    4.6 Example: classifying spam email with naïve Bayes
    4.6.1 Prepare: tokenizing text
    4.6.2 Test: cross validation with naïve Bayes
    4.7 Example: using naïve Bayes to reveal local attitudes from personal ads
    4.7.1 Collect: importing RSS feeds
    4.7.2 Analyze: displaying locally used words
    4.8 Summary
    Logistic regression
    5.1 Classification with logistic regression and the sigmoid function: a tractable step function
    5.2 Using optimization to find the best regression coefficients
    5.2.1 Gradient ascent
    5.2.2 Train: using gradient ascent to find the best parameters
    5.2.3 Analyze: plotting the decision boundary
    5.2.4 Train: stochastic gradient ascent
    5.3 Example: estimating horse fatalities from colic
    5.3.1 Prepare: dealing with missing values in the data
    5.3.2 Test: classifying with logistic regression
    5.4 Summary
    Support vector machines
    6.1 Separating data with the maximum margin
    6.2 Finding the maximum margin
    6.2.1 Framing the optimization problem in terms of our classifier
    6.2.2 Approaching SVMs with our general framework
    6.3 Efficient optimization with the SMO algorithm
    6.3.1 Platt’s SMO algorithm
    6.3.2 Solving small datasets with the simplified SMO
    6.4 Speeding up optimization with the full Platt SMO
    6.5 Using kernels for more complex data
    6.5.1 Mapping data to higher dimensions with kernels
    6.5.2 The radial bias function as a kernel
    6.5.3 Using a kernel for testing
    6.6 Example: revisiting handwriting classification
    6.7 Summary
    Improving classification with the AdaBoost meta-algorithm
    7.1 Classifiers using multiple samples of the dataset
    7.1.1 Building classifiers from randomly resampled data: bagging
    7.1.2 Boosting
    7.2 Train: improving the classifier by focusing on errors
    7.3 Creating a weak learner with a decision stump
    7.4 Implementing the full AdaBoost algorithm
    7.5 Test: classifying with AdaBoost
    7.6 Example: AdaBoost on a difficult dataset
    7.7 Classification imbalance
    7.7.1 Alternative performance metrics: precision, recall, and ROC
    7.7.2 Manipulating the classifier’s decision with a cost function
    7.7.3 Data sampling for dealing with classification imbalance
    7.8 Summary
    Forecasting numeric values with regression
    Predicting numeric values: regression
    8.1 Finding best-fit lines with linear regression
    8.2 Locally weighted linear regression
    8.3 Example: predicting the age of an abalone
    8.4 Shrinking coefficients to understand our data
    8.4.1 Ridge regression
    8.4.2 The lasso
    8.4.3 Forward stagewise regression
    8.5 The bias/variance tradeoff
    8.6 Example: forecasting the price of LEGO sets
    8.6.1 Collect: using the Google shopping API
    8.6.2 Train: building a model
    8.7 Summary
    Tree-based regression
    9.1 Locally modeling complex data
    9.2 Building trees with continuous and discrete features
    9.3 Using CART for regression
    9.3.1 Building the tree
    9.3.2 Executing the code
    9.4 Tree pruning
    9.4.1 Prepruning
    9.4.2 Postpruning
    9.5 Model trees
    9.6 Example: comparing tree methods to standard regression
    9.7 Using Tkinter to create a GUI in Python
    9.7.1 Building a GUI in Tkinter
    9.7.2 Interfacing Matplotlib and Tkinter
    9.8 Summary
    Unsupervised learning
    Grouping unlabeled items using k-means clustering
    10.1 The k-means clustering algorithm
    10.2 Improving cluster performance with postprocessing
    10.3 Bisecting k-means
    10.4 Example: clustering points on a map
    10.4.1 The Yahoo! PlaceFinder API
    10.4.2 Clustering geographic coordinates
    10.5 Summary
    Association analysis with the Apriori algorithm
    11.1 Association analysis
    11.2 The Apriori principle
    11.3 Finding frequent itemsets with the Apriori algorithm
    11.3.1 Generating candidate itemsets
    11.3.2 Putting together the full Apriori algorithm
    11.4 Mining association rules from frequent item sets
    11.5 Example: uncovering patterns in congressional voting
    11.5.1 Collect: build a transaction data set of congressional voting records
    11.5.2 Test: association rules from congressional voting records
    11.6 Example: finding similar features in poisonous mushrooms
    11.7 Summary
    Efficiently finding frequent itemsets with FP-growth
    12.1 FP-trees: an efficient way to encode a dataset
    12.2 Build an FP-tree
    12.2.1 Creating the FP-tree data structure
    12.2.2 Constructing the FP-tree
    12.3 Mining frequent items from an FP-tree
    12.3.1 Extracting conditional pattern bases
    12.3.2 Creating conditional FP-trees
    12.4 Example: finding co-occurring words in a Twitter feed
    12.5 Example: mining a clickstream from a news site
    12.6 Summary
    Additional tools
    Using principal component analysis to simplify data
    13.1 Dimensionality reduction techniques
    13.2 Principal component analysis
    13.2.1 Moving the coordinate axes
    13.2.2 Performing PCA in NumPy
    13.3 Example: using PCA to reduce the dimensionality of semiconductor manufacturing data
    13.4 Summary
    Simplifying data with the singular value decomposition
    14.1 Applications of the SVD
    14.1.1 Latent semantic indexing
    14.1.2 Recommendation systems
    14.2 Matrix factorization
    14.3 SVD in Python
    14.4 Collaborative filtering–based recommendation engines
    14.4.1 Measuring similarity
    14.4.2 Item-based or user-based similarity?
    14.4.3 Evaluating recommendation engines
    14.5 Example: a restaurant dish recommendation engine
    14.5.1 Recommending untasted dishes
    14.5.2 Improving recommendations with the SVD
    14.5.3 Challenges with building recommendation engines
    14.6 Example: image compression with the SVD
    14.7 Summary
    Big data and MapReduce
    15.1 MapReduce: a framework for distributed computing
    15.2 Hadoop Streaming
    15.2.1 Distributed mean and variance mapper
    15.2.2 Distributed mean and variance reducer
    15.3 Running Hadoop jobs on Amazon Web Services
    15.3.1 Services available on AWS
    15.3.2 Getting started with Amazon Web Services
    15.3.3 Running a Hadoop job on EMR
    15.4 Machine learning in MapReduce
    15.5 Using mrjob to automate MapReduce in Python
    15.5.1 Using mrjob for seamless integration with EMR
    15.5.2 The anatomy of a MapReduce script in mrjob
    15.6 Example: the Pegasos algorithm for distributed SVMs
    15.6.1 The Pegasos algorithm
    15.6.2 Training: MapReduce support vector machines with mrjob
    15.7 Do you really need MapReduce?
    15.8 Summary
    appendix A: Getting started with Python
    A.1 Installing Python
    A.1.1 Windows
    A.1.2 Mac OS X
    A.1.3 Linux
    A.2 A quick introduction to Python
    A.2.1 Collection types
    A.2.2 Control structures
    A.2.3 List comprehensions
    A.3 A quick introduction to NumPy
    A.4 Beautiful Soup
    A.5 Mrjob
    A.6 Vote Smart
    A.7 Python-Twitter
    appendix B: Linear algebra
    B.1 Matrices
    B.2 Matrix inverse
    B.3 Norms
    B.4 Matrix calculus
    appendix C: Probability refresher
    C.1 Intro to probability
    C.2 Joint probability
    C.3 Basic rules of probability
    appendix D: Resources
    index
    Numerics
    A
    B
    C
    D
    E
    F
    G
    H
    I
    J
    K
    L
    M
    N
    O
    P
    R
    S
    T
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    V
    W
    X
    Y
    IN ACTION Peter Harrington M A N N I N G
    Machine Learning in Action
    Machine Learning in Action PETER HARRINGTON M A N N I N G Shelter Island
    For online information and ordering of this and other Manning books, please visit www.manning.com. The publisher offers discounts on this book when ordered in quantity. For more information, please contact Special Sales Department Manning Publications Co. 20 Baldwin Road PO Box 261 Shelter Island, NY 11964 Email: orders@manning.com ©2012 by Manning Publications Co. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by means electronic, mechanical, photocopying, or otherwise, without prior written permission of the publisher. Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in the book, and Manning Publications was aware of a trademark claim, the designations have been printed in initial caps or all caps. Recognizing the importance of preserving what has been written, it is Manning’s policy to have the books we publish printed on acid-free paper, and we exert our best efforts to that end. Recognizing also our responsibility to conserve the resources of our planet, Manning books are printed on paper that is at least 15 percent recycled and processed without the use of elemental chlorine. Manning Publications Co.Development editor:Jeff Bleiel 20 Baldwin Road PO Box 261 Shelter Island, NY 11964 Copyeditor: Linda Recktenwald Proofreader: Maureen Spencer Typesetter: Gordan Salinovic Technical proofreaders: Tricia Hoffman, Alex Ott Cover designer: Marija Tudor ISBN 9781617290183 Printed in the United States of America 1 2 3 4 5 6 7 8 9 10 – MAL – 17 16 15 14 13 12
    To Joseph and Milo
    PART 1 CLASSIFICATION ...............................................................1 brief contents 1 ■ Machine learning basics 3 2 ■ Classifying with k-Nearest Neighbors 18 3 ■ Splitting datasets one feature at a time: decision trees 37 4 ■ Classifying with probability theory: naïve Bayes 61 5 ■ Logistic regression 83 6 ■ Support vector machines 101 7 ■ Improving classification with the AdaBoost meta-algorithm 129 PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION..............151 8 ■ Predicting numeric values: regression 153 9 ■ Tree-based regression 179 PART 3 UNSUPERVISED LEARNING...............................................205 10 ■ Grouping unlabeled items using k-means clustering 207 11 ■ Association analysis with the Apriori algorithm 224 12 ■ Efficiently finding frequent itemsets with FP-growth 248 vii
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