Learning Data Mining with Python

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The result is shown in the next graph. It can be quite clearly seen that the main source of error is U being mistaken for an H nearly every single time! Chapter 8 The letter U appears in 17 percent of words in our list. For each word that a U appears in, we can expect this to be wrong. U actually appears more often than H (which is in around 11 percent of words), indicating we could get a cheap (although possibly not a robust) boost in accuracy by changing any H prediction into a U. In the next section, we will do something a bit smarter and actually use the dictionary to search for similar words. [ 179 ]
Beating CAPTCHAs <strong>with</strong> Neural Networks Improving accuracy using a dictionary Rather than just returning the given prediction, we can check whether the word actually exists in our dictionary. If it does, then that is our prediction. If it isn't in the dictionary, we can try and find a word that is similar to it and predict that instead. Note that this strategy relies on our assumption that all CAPTCHA words will be valid English words, and therefore this strategy wouldn't work for a random sequence of characters. This is one reason why some CAPTCHAs don't use words. There is one issue here—how do we determine the closest word? There are many ways to do this. For instance, we can compare the lengths of words. Two words that have a similar length could be considered more similar. However, we commonly consider words to be similar if they have the same letters in the same positions. This is where the edit distance comes in. Ranking mechanisms for words The Levenshtein edit distance is a commonly used method for comparing two short strings to see how similar they are. It isn't very scalable, so it isn't commonly used for very long strings. The edit distance computes the number of steps it takes to go from one word to another. The steps can be one of the following three actions: 1. Insert a new letter into the word at any position. 2. Delete any letter from the word. 3. Substitute a letter for another one. The minimum number of actions needed to transform the first word into the second is given as the distance. Higher values indicate that the words are less similar. This distance is available in NLTK as nltk.metrics.edit_distance. We can call it using on two strings and it returns the edit distance: from nltk.metrics import edit_distance steps = edit_distance("STEP", "STOP") print("The number of steps needed is: {0}".format(steps)) When used <strong>with</strong> different words, the edit distance is quite a good approximation to what many people would intuitively feel are similar words. The edit distance is great for testing spelling mistakes, dictation errors, and name matching (where you can mix up your Marc and Mark spelling quite easily). [ 180 ]
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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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Page 162 and 163: Chapter 7 Make sure the filename is
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Page 168 and 169: Chapter 7 Creating a graph Now, we
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Page 178 and 179: Chapter 7 Optimizing criteria Our a
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Page 188 and 189: Chapter 8 The combination of an app
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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 204 and 205: Chapter 8 However, it isn't very go
Page 206: Chapter 8 Summary In this chapter,
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Page 217 and 218: Authorship Attribution "instead", "
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Page 223 and 224: Authorship Attribution We can reuse
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Page 227 and 228: Authorship Attribution We then reco
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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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Chapter 11 Summary In this chapter,
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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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