www.allitebooks.com

Recommendations

Info

Chapter 4 – Recommending Movies Using Affinity Analysis Appendix New datasets http://www2.informatik.uni-freiburg.de/~cziegler/BX/ There are many recommendation-based datasets that are worth investigating, each with its own issues. For example, the Book-Crossing dataset contains more than 278,000 users and over a million ratings. Some of these ratings are explicit (the user did give a rating), while others are more implicit. The weighting to these implicit ratings probably shouldn't be as high as for explicit ratings. The music website www.last.fm has released a great dataset for music recommendation: http://www.dtic.upf.edu/~ocelma/ MusicRecommendationDataset/. There is also a joke recommendation dataset! See here: http://eigentaste. berkeley.edu/dataset/. The Eclat algorithm http://www.borgelt.net/eclat.html The APriori algorithm implemented in this chapter is easily the most famous of the association rule mining graphs, but isn't necessarily the best. Eclat is a more modern algorithm that can be implemented relatively easily. Chapter 5 – Extracting Features with Transformers Adding noise In this chapter, we covered removing noise to improve features; however, improved performance can be obtained for some datasets by adding noise. The reason for this is simple—it helps stop overfitting by forcing the classifier to generalize its rules a little (although too much noise will make the model too general). try implementing a Transformer that can add a given amount of noise to a dataset. Test that out on some of the datasets from UCI ML and see if it improves test-set performance. [ 301 ]
Next Steps… Vowpal Wabbit http://hunch.net/~vw/ Vowpal Wabbit is a great project, providing very fast feature extraction for textbased problems. It comes with a Python wrapper, allowing you to call it from with Python code. Test it out on large datasets, such as the one we used in Chapter 12, Working with Big Data. Chapter 6 – Social Media Insight Using Naive Bayes Spam detection http://scikit-learn.org/stable/modules/model_evaluation.html#scoringparameter Using the concepts in this chapter, you can create a spam detection method that is able to view a social media post and determine whether it is spam or not. Try this out by first creating a dataset of spam/not-spam posts, implementing the text mining algorithms, and then evaluating them. One important consideration with spam detection is the false-positive/false-negative ratio. Many people would prefer to have a couple of spam messages slip through, rather than miss out on a legitimate message because the filter was too aggressive in stopping the spam. In order to turn your method for this, you can use a Grid Search with the f1-score as the evaluation criteria. See the above link for information on how to do this. Natural language processing and part-ofspeech tagging http://www.nltk.org/book/ch05.html The techniques we used in this chapter were quite lightweight compared to some of the linguistic models employed in other areas. For example, part-of-speech tagging can help disambiguate word forms, allowing for higher accuracy. The book that comes with NLTK has a chapter on this, linked above. The whole book is well worth reading too. [ 302 ]
Page 1 and 2:
[ 1 ] www.allitebooks.com
Page 3 and 4:
Learning Data Mining with Python Co
Page 5 and 6:
About the Author Robert Layton has
Page 7 and 8:
Christophe Van Gysel is pursuing a
Page 9 and 10:
www.allitebooks.com
Page 11 and 12:
Table of Contents Preprocessing usi
Page 13 and 14:
Table of Contents Chapter 7: Discov
Page 15 and 16:
Table of Contents GPU optimization
Page 18 and 19:
Preface If you have ever wanted to
Page 20 and 21:
What you need for this book It shou
Page 22 and 23:
Preface Reader feedback Feedback fr
Page 24 and 25:
Getting Started with Data Mining We
Page 26 and 27:
Chapter 1 In the preceding dataset,
Page 28 and 29:
After you have the above "Hello, wo
Page 30 and 31:
Chapter 1 Windows users may need to
Page 32 and 33:
Chapter 1 The dataset we are going
Page 34 and 35:
Chapter 1 As an example, we will co
Page 36 and 37:
We get the names of the features fo
Page 38 and 39:
Chapter 1 Two rules are near the to
Page 40 and 41:
Chapter 1 The scikit-learn library
Page 42 and 43:
We then iterate over all the sample
Page 44 and 45:
Chapter 1 Overfitting is the proble
Page 46:
Chapter 1 Summary In this chapter,
Page 49 and 50:
Classifying with scikit-learn Estim
Page 51 and 52:
Page 53 and 54:
Page 55 and 56:
Page 57 and 58:
Page 59 and 60:
Page 61 and 62:
Page 63 and 64:
Page 65 and 66:
Predicting Sports Winners with Deci
Page 67 and 68:
Page 69 and 70:
Page 71 and 72:
Page 73 and 74:
Page 75 and 76:
Page 77 and 78:
Page 79 and 80:
Page 81 and 82:
Page 84 and 85:
Recommending Movies Using Affinity
Page 86 and 87:
Chapter 4 The classic algorithm for
Page 88 and 89:
Chapter 4 When loading the file, we
Page 90 and 91:
Chapter 4 We will sample our datase
Page 92 and 93:
Chapter 4 Implementation On the fir
Page 94 and 95:
Chapter 4 We want to break out the
Page 96 and 97:
The process starts by creating dict
Page 98 and 99:
movie_name_data.columns = ["MovieID
Page 100 and 101:
To do this, we will compute the tes
Page 102 and 103:
Chapter 4 - Train Confidence: 1.000
Page 104 and 105:
Extracting Features with Transforme
Page 106 and 107:
Chapter 5 Thought should always be
Page 108 and 109:
Chapter 5 Other features describe a
Page 110 and 111:
Chapter 5 Similarly, we can convert
Page 112 and 113:
Chapter 5 [18, 19, 20], [21, 22, 23
Page 114 and 115:
Chapter 5 Next, we create our trans
Page 116 and 117:
Chapter 5 This returns a different
Page 118 and 119:
Also, we want to set the final colu
Page 120 and 121:
Chapter 5 The downside to transform
Page 122 and 123:
Chapter 5 A transformer is akin to
Page 124 and 125:
We can then create an instance of t
Page 126:
Chapter 5 Putting it all together N
Page 129 and 130:
Social Media Insight Using Naive Ba
Page 131 and 132:
Page 133 and 134:
Page 135 and 136:
Page 137 and 138:
Page 139 and 140:
Page 141 and 142:
Page 143 and 144:
Page 145 and 146:
Page 147 and 148:
Page 149 and 150:
Page 151 and 152:
Page 153 and 154:
Page 155 and 156:
Page 158 and 159:
Discovering Accounts to Follow Usin
Page 160 and 161:
Chapter 7 Next, we will need a list
Page 162 and 163:
Chapter 7 Make sure the filename is
Page 164 and 165:
Chapter 7 cursor = results['next_cu
Page 166 and 167:
Chapter 7 Next, we are going to rem
Page 168 and 169:
Chapter 7 Creating a graph Now, we
Page 170 and 171:
Chapter 7 As you can see, it is ver
Page 172 and 173:
Chapter 7 Next, we will only add th
Page 174 and 175:
Chapter 7 The difference in this gr
Page 176 and 177:
Chapter 7 We can graph the entire s
Page 178 and 179:
Chapter 7 Optimizing criteria Our a
Page 180 and 181:
Chapter 7 Next, we need to get the
Page 182 and 183:
• method='nelder-mead': This is u
Page 184 and 185:
Beating CAPTCHAs with Neural Networ
Page 186 and 187:
Chapter 8 The red lines indicate th
Page 188 and 189:
Chapter 8 The combination of an app
Page 190 and 191:
Chapter 8 Next we set the font of t
Page 192 and 193:
Chapter 8 We can then extract the s
Page 194 and 195:
Chapter 8 Our targets are integer v
Page 196 and 197:
Chapter 8 Then we iterate over our
Page 198 and 199:
Chapter 8 From these predictions, w
Page 200 and 201:
Chapter 8 This code correctly predi
Page 202 and 203:
The result is shown in the next gra
Page 204 and 205:
Chapter 8 However, it isn't very go
Page 206:
Chapter 8 Summary In this chapter,
Page 209 and 210:
Authorship Attribution Attributing
Page 211 and 212:
Authorship Attribution If we cannot
Page 213 and 214:
Authorship Attribution After taking
Page 215 and 216:
Authorship Attribution This dataset
Page 217 and 218:
Authorship Attribution "instead", "
Page 219 and 220:
Authorship Attribution Support vect
Page 221 and 222:
Authorship Attribution Kernels When
Page 223 and 224:
Authorship Attribution We can reuse
Page 225 and 226:
Authorship Attribution With our dat
Page 227 and 228:
Authorship Attribution We then reco
Page 229 and 230:
Authorship Attribution If it doesn'
Page 231 and 232:
Authorship Attribution Finally, we
Page 234 and 235:
Clustering News Articles In most of
Page 236 and 237:
Chapter 10 API Endpoints are the ac
Page 238 and 239:
The token object is just a dictiona
Page 240 and 241:
Chapter 10 We then create a list to
Page 242 and 243:
Chapter 10 We are going to use MD5
Page 244 and 245:
Chapter 10 Next, we develop the cod
Page 246 and 247:
Chapter 10 We use clustering techni
Page 248 and 249:
Chapter 10 The k-means algorithm is
Page 250 and 251:
Chapter 10 We only fit the X matrix
Page 252 and 253:
Chapter 10 We then print out the mo
Page 254 and 255:
Chapter 10 Our function definition
Page 256 and 257:
Chapter 10 The result from the prec
Page 258 and 259:
Chapter 10 Implementation Putting a
Page 260 and 261:
Chapter 10 Neural networks can also
Page 262 and 263:
We then call the partial_fit functi
Page 264 and 265:
Classifying Objects in Images Using
Page 266 and 267:
Chapter 11 This dataset comes from
Page 268 and 269:
You can change the image index to s
Page 270 and 271:
Chapter 11 Each of these issues has
Page 272 and 273:
Chapter 11 Using Theano, we can def
Page 274 and 275: Chapter 11 Building a neural networ
Page 276 and 277: Chapter 11 Finally, we create Thean
Page 278 and 279: Chapter 11 return [image,] return s
Page 280 and 281: Chapter 11 Next, we define how the
Page 282 and 283: Chapter 11 Getting your code to run
Page 284 and 285: Chapter 11 Setting up the environme
Page 286 and 287: This will unzip only one Coval.otf
Page 288 and 289: Chapter 11 First we create the laye
Page 290 and 291: Chapter 11 Finally, we set the verb
Page 292: Chapter 11 Summary In this chapter,
Page 295 and 296: Working with Big Data Big data What
Page 297 and 298: Working with Big Data Governments a
Page 299 and 300: Working with Big Data We start by c
Page 301 and 302: Working with Big Data The final ste
Page 303 and 304: Working with Big Data Getting the d
Page 305 and 306: Working with Big Data If we aren't
Page 307 and 308: Working with Big Data Before we sta
Page 309 and 310: Working with Big Data The first val
Page 311 and 312: Working with Big Data This gives us
Page 313 and 314: Working with Big Data Next, we crea
Page 315 and 316: Working with Big Data Then, make a
Page 317 and 318: Working with Big Data Left-click th
Page 319 and 320: Working with Big Data The result is
Page 321 and 322: Next Steps… Extending the IPython
Page 323: Next Steps… Chapter 3: Predicting
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 and 338: NetworkX about 145 defining 303 URL
Page 339 and 340: scikit-learn package references 305
Page 342 and 343: Thank you for buying Learning Data
Page 344: Learning Python Data Visualization
show all

www.allitebooks.com

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?