Machine learning in action

382 Pages·2012·4.16 MB·English

by  Harrington P.

IN ACTION
Peter Harrington
M A N N I N G
Machine Learning in Action

Machine Learning in Action
PETER HARRINGTON
MANNING
Shelter Island
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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

brief contents
P 1 C ...............................................................1
ART LASSIFICATION
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
P 2 F ..............151
ART ORECASTING NUMERIC VALUES WITH REGRESSION
8 ■ Predicting numeric values: regression 153
9 ■ Tree-based regression 179
P 3 U ...............................................205
ART NSUPERVISED LEARNING
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
viii BRIEF CONTENTS
P 4 A .......................................................267
ART DDITIONAL TOOLS
13 ■ Using principal component analysis to simplify data 269
14 ■ Simplifying data with the singular value
decomposition 280
15 ■ Big data and MapReduce 299
contents
preface xvii
acknowledgments xix
about this book xxi
about the author xxv
about the cover illustration xxvi
P 1 C ...................................................1
ART LASSIFICATION
1 Machine learning basics 3
1.1 What is machine learning? 5
Sensors and the data deluge 6 ■ Machine learning will be more
important in the future 7
1.2 Key terminology 7
1.3 Key tasks of machine learning 10
1.4 How to choose the right algorithm 11
1.5 Steps in developing a machine learning application 11
1.6 Why Python? 13
Executable pseudo-code 13 ■ Python is popular 13 ■ What
Python has that other languages don’t have 14 ■ Drawbacks 14
1.7 Getting started with the NumPy library 15
1.8 Summary 17
ix