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Chapter 4 - Train Confidence: 1.000 - Test Confidence: 0.971 Rule #5 Rule: If a person recommends Shawshank Redemption, The (1994), Toy Story (1995), Twelve Monkeys (1995), Empire Strikes Back, The (1980), Fugitive, The (1993), Star Wars (1977) they will also recommend Return of the Jedi (1983) - Train Confidence: 1.000 - Test Confidence: 0.900 The second rule, for instance, has a perfect confidence in the training data, but it is only accurate in 60 percent of cases for the test data. Many of the other rules in the top 10 have high confidences in test data though, making them good rules for making recommendations. If you are looking through the rest of the rules, some will have a test confidence of -1. Confidence values are always between 0 and 1. This value indicates that the particular rule wasn't found in the test dataset at all. Summary In this chapter, we performed affinity analysis in order to recommend movies based on a large set of reviewers. We did this in two stages. First, we found frequent itemsets in the data using the Apriori algorithm. Then, we created association rules from those itemsets. The use of the Apriori algorithm was necessary due to the size of the dataset. While in Chapter 1, Getting Started With Data Mining, we used a brute-force approach, the exponential growth in the time needed to compute those rules required a smarter approach. This is a common pattern for data mining: we can solve many problems in a brute force manner, but smarter algorithms allow us to apply the concepts to larger datasets. We performed training on a subset of our data in order to find the association rules, and then tested those rules on the rest of the data—a testing set. From what we discussed in the previous chapters, we could extend this concept to use cross-fold validation to better evaluate the rules. This would lead to a more robust evaluation of the quality of each rule. [ 79 ]
Recommending Movies Using Affinity Analysis So far, all of our datasets have been in terms of features. However, not all datasets are "pre-defined" in this way. In the next chapter, we will look at scikit-learn's transformers (they were introduced in Chapter 3, Predicting Sports Winners with Decision Trees) as a way to extract features from data. We will discuss how to implement our own transformers, extend existing ones, and concepts we can implement using them. [ 80 ]
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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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Page 86 and 87: Chapter 4 The classic algorithm for
Page 88 and 89: Chapter 4 When loading the file, we
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Page 92 and 93: Chapter 4 Implementation On the fir
Page 94 and 95: Chapter 4 We want to break out the
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Page 110 and 111: Chapter 5 Similarly, we can convert
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Social Media Insight Using Naive Ba
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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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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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Chapter 11 First we create the laye
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Chapter 11 Finally, we set the verb
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Working with Big Data Big data What
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Working with Big Data Governments a
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Working with Big Data We start by c
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Working with Big Data The final ste
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Working with Big Data Getting the d
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Working with Big Data If we aren't
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Working with Big Data Before we sta
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Working with Big Data The first val
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Working with Big Data This gives us
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Working with Big Data Next, we crea
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Working with Big Data Then, make a
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Working with Big Data Left-click th
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Working with Big Data The result is
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Next Steps… Extending the IPython
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Next Steps… Chapter 3: Predicting
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Next Steps… Vowpal Wabbit http://
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Next Steps… Deeper networks These
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Next Steps… Real-time clusterings
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Next Steps… More resources Kaggle
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authorship, attributing 185-188 AWS
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feature extraction about 82 common
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NetworkX about 145 defining 303 URL
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scikit-learn package references 305
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Thank you for buying Learning Data
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Learning Python Data Visualization
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