Machine learning in action:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Shelter Island, NY
Manning
2012
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | XXVI, 354 S. Ill., graph. Darst. 24 cm |
| ISBN: | 9781617290183 1617290181 |
Internformat
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Datensatz im Suchindex
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| adam_text | contents preface xvii acknowledgments xix about էհե book xxi about the author xxv about the caver illustration Part 1 xxvi Classification «····»···»··«*· «···········»*···»«·»·*···«··»· Tf Machine learning basics 3 1.1 What is machine learning? Sensors and the data deluge important in the future 7 5 6 · Machine learning will be more 1.2 Key terminology 7 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? 10 11 11 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 1.8 Summary 17 ix 15 1
CONTENTS x Ql Classifying ivith k-Nearest Neighbors “ 2.1 18 Classifying with distance measurements 19 Prepare: importing data with Python 21 · Putting the kNN classification algorithm into action 23 * How to test a classifier 24 2.2 Example: improving matches from a dating site with kNN 24 Prepare: parsing datafrom a textfile 25 · Analyze: creating scatter plots with Matplotlib 27· Prepare: normalizing numeric values 29* Test: testing the classifier as a whole program 31 * Use: putting together a useful system 32 2.3 Example: a handwriting recognition system Prepare: converting images into test vectors handwritten digits 35 2.4 Summary 33 33 * Test: kATN on 36 Դ Splitting datasets one feature at a ūme: decision trees 37 3.1 Tree construction Information gain the tree 46 3.2 39 40 · Splitting the dataset 43 * Recursively building Plotting trees in Python with Matplotlib annotations Matplotlib annotations 3.3 49* Constructing a tree of annotations Testing and storing the classifier Test: using the tree for classification tree 57 48 51 56 56 * Use: persisting the decision 3.4 Example: using decision trees to predict contact lens type 3.5 Summary 57 59 Classifying with probability theory: naïve Bayes 4.1 Classifying with Bayesian decision theory 4.2 Conditional probability 4.3 Classifying with conditional probabilities 65 4.4 Document classification with naïve Bayes 65 4.5 Classifying text with Python 61 62 63 67 Prepare: making word vectors from text 67 * Train: calculating probabilities from word vectors 69 * Test: modifying the classifierfor realworld conditions 71 *
Prepare: the bag-of-words document model 73 4.6 Example: classifying spam email with naïve Bayes Prepare: tokenizing text 74 74 * Test: cross validation with naïve Bayes 75
xi CONTENTS 4.7 Example: using naïve Bayes to reveal local attitudes from personal ads 77 Collect: importing RSSfeeds words 80 4.8 Summary 82 Logistic regression 5.1 78 · Analyze: displaying locally used 83 Classification with logistic regression and the sigmoid function: a tractable step function 84 Using optimization to find the best regression coefficients 86 5.2 Gradient ascent 86 · Train: using gradient ascent to find the best parameters 88 · Analyze: plotting the decision boundary 90 Train: stochastic gradient ascent 91 5.3 Example: estimating horse fatalities from colic Prepare: dealing with missing values in the data with logistic regression 98 5.4 Summary 97 · Test: classifying 100 Support vedor machines 101 6.1 Separating data with the maximum margin 6.2 Finding the maximum margin 102 104 Framing the optimization problem in terms of our classifier Approaching SVMs with our generalframework 106 6.3 96 104 Efficient optimization with the SMO algorithm Platt’s SMO algorithm SMO 107 106 106· Solving small datasets with the simplified 6.4 Speeding up optimization with the full Platt SMO 6.5 Using kernels for more complex data 112 118 Mapping data to higher dimensions with kernels 118 · The radial bias function as a kernel 119· Using a kernelfor testing 122 6.6 Example: revisiting handwriting classification 6.7 Summary 125 127 Improving classification with the AdaBoost meta-algorithm 7.1 Classifiers using multiple samples of the dataset Building classifiers from randomly resampled data: bagging Boosting 131 7.2 130 130 Train: improving the classifier by focusing on
errors 131 129
xii CONTENTS 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 133 136 139 140 142 Alternative performance metrics: precision, recall, and ROC 143 Manipulating the classifier’s decision with a costfunction 147 Data samplingfor dealing with classification imbalance 148 7.8 Summary 148 Part 2 Forecasting numeric values with regression ....151 £ 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 8.4 154 160 163 Shrinking coefficients to understand our data Ridge regression regression 167 164 The lasso ■ 167 * The bias/variance tradeoff 170 8.6 Example: forecasting the price of LEGO sets 8.7 Summary 173 164 Forward stagewise 8.5 Collect: using the Google shopping API 9 153 ■ 172 Train: building a model 177 179 Tree-based regression 9.1 Locally modeling complex data 9.2 Building trees with continuous and discrete features 9.3 Using CART for regression Building the tree 9.4 184 Tree pruning Prepruning 188 ■ 180 181 184 Executing the code 186 188 Postpruning * 190 9.5 Model trees 9.6 Example: comparing tree methods to standard regression 9.7 192 Using Tkinter to create a GUI in Python Building a GUI in Tkinter 9.8 174 Summary 203 195 198 199 · Interfacing Matplotlib and Tkinter 201
xiii CONTENTS Part 3 Unsupervised learning.......................................................... 205 10 Grouping unlabeled items using k-means clustering 207 10.1 10.2 The k-means clustering algorithm 208 Improving cluster performance with postprocessing 10.3 Bisecting k-means 10.4 Example: clustering points on a map 213 214 The Yahoo! PlaceFinder API coordinates 220 217 218■ Clustering geographic 10.5 Summary 223 ƒ Association analysis with the Apriori algorithm 224 * 11.1 Association analysis 11.2 The Apriori principle 11.3 225 Finding frequent itemsets with the Apriori algorithm Generating candidate itemsets algorithm 231 11.4 11.5 226 229 ■ 228 Putting together thefull Apriori Mining association rules from frequent item sets 233 Example: uncovering patterns in congressional voting 237 Collect: build a transaction data set of congressional voting records 238 ■ Test: association rules from congressional voting records 243 11.6 11.7 Example: finding similar features in poisonous mushrooms 245 Summary 246 Ռ Efficiently findingfrequent itemsets with FP-growth ^ 248 12.1 FP-trees: an efficient way to encode a dataset 249 12.2 Build an FP-tree 251 Creating the FP-tree data structure 12.3 251 · Constructing the FP-tree Mining frequent items from an FP-tree Extracting conditional pattern bases FP-trees 258 257 * 256 Creating conditional 12.4 Example: finding co-occuriing words in a Twitter feed 12.5 Example: mining a clickstream from a news site 12.6 Summary 265 252 264 260
xiv CONTENTS Part 4 Additional tools 267 Using principal component analysu to simplify data 13.1 Dimensionality reduction techniques 13.2 Principal component analysis 271 Moving the coordinate axes 13.3 13.4 հ1 * 269 270 271 · PerformingPCA in NumPy 273 Example: using PGA to reduce the dimensionality of semiconductor manufacturing data 275 Summary 278 Simplifying data with the ńngular value decomposition 14.1 Applications of the SVD Latent semantic indexing 280 281 281 · Recommendation systems 282 14.2 Matrix factorization 283 14.3 SVD in Python 14.4 Collaborative filtering-based recommendation engines 284 Measuring similarity 287 · Item-based or user-based similarity Ì Evaluating recommendation engines 289 14.5 286 289 Example: a restaurant dish recommendation engine 290 Recommending untasted dishes 290· Improving recommendations with the SVD 292 · ChaUengps with building recommendation engines 295 14.6 Example: image compression with the SVD 14.7 Summary 298 Big data and MapReduce 295 299 15.1 MapReduce: a framework for distributed computing 15.2 Hadoop Streaming 302 Distributed mean and variance mapper variance reducer 304 15.3 300 303 · Distributed mean and Running Hadoop jobs on Amazon Web Services 305 Services available on AWS 305 · Getting started with Amazon Web Services 306 · Running a Hadoop job on EMR 307 15.4 Machine learning in MapReduce 312 15.5 Using mrjob to automate MapReduce in Python Using mrjobfor seamless integration with EMR MapReduce script in mrjob 314 313 313 · The anatomy of a
CONTENTS 15.6 Example: the Pegasos algorithm for distributed SVMs The Pegasos algorithm 317 machines with mrjob 318 » Training: MapReduce support vector 15.7 Do you really need MapReduce? 15.8 Summary appendix A appendix В appendix C appendix D 323 Getting started with Python Linear algebra 335 Probability refresher 341 Resources 345 index 347 325 322 316
PYTHON/MACHINE LEARNING Machine Learning inaction Peter Harrington machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interesting or useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. A Machine Learning in Action is a clearly written tutorial for devel opers. It avoids academic language and takes you straight to the techniques you’ll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data process ing, data analysis, and data visualization in code you can reuse. You’ll understand the concepts and how they fit in with tacti cal tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. What s Inside • A no-nonsense introduction • Examples showing common ML tasks • Everyday data analysis • Implementing classic algorithms like Apriori and Adaboost Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. Peter Harrington is a professional developer and data scientist. He holds five US patents and his work has been published in numerous academic journals. For access to the book s forum and a free eBook for owners of this book, go to manning.com/MachineLearninginAction
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