Learning Data Mining with Python

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preprocessing, using pipelines about 35, 36 example 36, 37 features 35 features, of animal 35 standard preprocessing 37 workflow, creating 38 pricing alerts URL 295 Principal Component Analysis (PCA) 96, 97 prior belief 122 probabilistic graphical models URL 308 probabilities computing 290 programmers, for Python language URL 4 Project Gutenberg URL 189 Pydoop about 307 URL 307 Pylearn2 about 306 URL 306 Python defining 106 installing 3, 4 URL 3 using 3 Python 3.4 3 Q quotequail package 205 R RandomForestClassifier 56 random forests about 26 applying 56, 57 defining 54, 55 ensembles, working 55 new features, engineering 58 parameters 56 reasons, feature selection complexity, reducing 88 noise, reducing 88 readable models, creating 88 recall 129 recommendation engine building 307 URL 307 reddit about 212-215 references 213 URL 215 regularization URL 97 reinforcement learning URL 304 RESTful interface (Representational State Transfer) 213 rules confidence 10 finding 13 support 10 S sample size increasing 304 scikit-learn installing 6 URL 7 scikit-learn estimators algorithm, running 32, 33 dataset, loading 29-31 defining 25, 26 distance metrics 27, 28 fit() function 31 Nearest neighbors 26, 27 parameters, setting 33-35 predict() 25 predict() function 31 standard workflow, defining 31 scikit-learn library about 25 estimators 25 pipelines 25 transformers 25 [ 315 ]
scikit-learn package references 305 sepal length 16 sepal width 16 shapes adding, CAPTCHAs URL 303 Silhouette Coefficient about 155 computing 155 parameters 158 similarity graph creating 147-151 SNAP URL 303 softmax nonlinearity 252 Spam detection references 302 spam filter 129 sparse matrix 27 sparse matrix format 66 sports outcome prediction about 49 features 50 stacking 53 standings loading 50-54 standings data obtaining 50 URL 50 Stratified K Fold 32 style sheets 218 stylometry 186 subgraphs connected components 151-153 criteria, optimizing 155-159 finding 151 subreddits 212, 215 SVMs about 196 classifying with 197 kernels 198 URL 197 system building, for taking image as input 242-244 T temporal analysis 305 text about 106 extracting, from arbitrary websites 218 text-based datasets 105 text transformers bag-of-words model 118-120 defining 118 features 121 n-grams 120 word, counting in dataset 118-120 tf-idf 120 Theano about 248, 249 URL 260 using 249 Torch URL 306 train_feature_value() function 20 transformer creating 98 implementing 99, 100 transformer API 99 unit testing 101, 102 tutorial, Google URL 307 tutorial, Yahoo URL 307 tweets about 106 F1-score, used for evaluation 129, 130 features, obtaining from models 130, 132 loading 128, 129 Twitter follower information, obtaining from 140, 141 Twitter account URL 107 twitter documentation URL 107 [ 316 ]
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[ 1 ] www.allitebooks.com
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Learning Data Mining with Python Co
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About the Author Robert Layton has
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Christophe Van Gysel is pursuing a
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www.allitebooks.com
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Table of Contents Preprocessing usi
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Table of Contents Chapter 7: Discov
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Table of Contents GPU optimization
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Preface If you have ever wanted to
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What you need for this book It shou
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Preface Reader feedback Feedback fr
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Getting Started with Data Mining We
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Chapter 1 In the preceding dataset,
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After you have the above "Hello, wo
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Chapter 1 Windows users may need to
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Chapter 1 The dataset we are going
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Chapter 1 As an example, we will co
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We get the names of the features fo
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Chapter 1 Two rules are near the to
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Chapter 1 The scikit-learn library
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We then iterate over all the sample
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Chapter 1 Overfitting is the proble
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Chapter 1 Summary In this chapter,
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Classifying with scikit-learn Estim
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Predicting Sports Winners with Deci
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Recommending Movies Using Affinity
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Chapter 4 The classic algorithm for
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Chapter 4 When loading the file, we
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Chapter 4 We will sample our datase
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Chapter 4 Implementation On the fir
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Chapter 4 We want to break out the
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The process starts by creating dict
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movie_name_data.columns = ["MovieID
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To do this, we will compute the tes
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Chapter 4 - Train Confidence: 1.000
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Extracting Features with Transforme
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Chapter 5 Thought should always be
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Chapter 5 Other features describe a
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Chapter 5 Similarly, we can convert
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Chapter 5 [18, 19, 20], [21, 22, 23
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Chapter 5 Next, we create our trans
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Chapter 5 This returns a different
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Also, we want to set the final colu
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Chapter 5 The downside to transform
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Chapter 5 A transformer is akin to
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We can then create an instance of t
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Chapter 5 Putting it all together N
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Social Media Insight Using Naive Ba
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Discovering Accounts to Follow Usin
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Chapter 7 Next, we will need a list
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Chapter 7 Make sure the filename is
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Chapter 7 cursor = results['next_cu
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Chapter 7 Next, we are going to rem
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Chapter 7 Creating a graph Now, we
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Chapter 7 As you can see, it is ver
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Chapter 7 Next, we will only add th
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Chapter 7 The difference in this gr
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Chapter 7 We can graph the entire s
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Chapter 7 Optimizing criteria Our a
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Chapter 7 Next, we need to get the
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• method='nelder-mead': This is u
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Beating CAPTCHAs with Neural Networ
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Chapter 8 The red lines indicate th
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Chapter 8 The combination of an app
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Chapter 8 Next we set the font of t
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Chapter 8 We can then extract the s
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Chapter 8 Our targets are integer v
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Chapter 8 Then we iterate over our
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Chapter 8 From these predictions, w
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Chapter 8 This code correctly predi
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The result is shown in the next gra
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Chapter 8 However, it isn't very go
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Chapter 8 Summary In this chapter,
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Authorship Attribution Attributing
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Authorship Attribution If we cannot
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Authorship Attribution After taking
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Authorship Attribution This dataset
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Authorship Attribution "instead", "
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Authorship Attribution Support vect
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Authorship Attribution Kernels When
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Authorship Attribution We can reuse
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Authorship Attribution With our dat
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Authorship Attribution We then reco
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Authorship Attribution If it doesn'
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Authorship Attribution Finally, we
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Clustering News Articles In most of
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Chapter 10 API Endpoints are the ac
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The token object is just a dictiona
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Chapter 10 We then create a list to
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Chapter 10 We are going to use MD5
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Chapter 10 Next, we develop the cod
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Chapter 10 We use clustering techni
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Chapter 10 The k-means algorithm is
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Chapter 10 We only fit the X matrix
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Chapter 10 We then print out the mo
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Chapter 10 Our function definition
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Chapter 10 The result from the prec
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Chapter 10 Implementation Putting a
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Chapter 10 Neural networks can also
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We then call the partial_fit functi
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Classifying Objects in Images Using
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Chapter 11 This dataset comes from
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You can change the image index to s
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Chapter 11 Each of these issues has
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Chapter 11 Using Theano, we can def
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Chapter 11 Building a neural networ
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Chapter 11 Finally, we create Thean
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Chapter 11 return [image,] return s
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Chapter 11 Next, we define how the
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Chapter 11 Getting your code to run
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Chapter 11 Setting up the environme
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This will unzip only one Coval.otf
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Page 295 and 296: Working with Big Data Big data What
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Page 321 and 322: Next Steps… Extending the IPython
Page 323 and 324: Next Steps… Chapter 3: Predicting
Page 325 and 326: Next Steps… Vowpal Wabbit http://
Page 327 and 328: Next Steps… Deeper networks These
Page 329 and 330: Next Steps… Real-time clusterings
Page 331 and 332: Next Steps… More resources Kaggle
Page 333 and 334: authorship, attributing 185-188 AWS
Page 335 and 336: feature extraction about 82 common
Page 337: NetworkX about 145 defining 303 URL
Page 342 and 343: Thank you for buying Learning Data
Page 344: Learning Python Data Visualization
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Learning Data Mining with Python

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