Learning Data Mining with Python

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Chapter 5 Other features describe a dataset in terms of its components: • Frequency of a given subcomponent, such as a word in a book • Number of subcomponents and/or the number of different subcomponents • Average size of the subcomponents, such as the average sentence length Ordinal features allow us to perform ranking, sorting, and grouping of similar values. As we have seen in previous chapters, features can be numerical or categorical. Numerical features are often described as being ordinal. For example, three people, Alice, Bob and Charlie, may have heights of 1.5 m, 1.6 m and 1.7 m. We would say that Alice and Bob are more similar in height than are Alice and Charlie. The Adult dataset that we loaded in the last section contains examples of continuous, ordinal features. For example, the Hours-per-week feature tracks how many hours per week people work. Certain operations make sense on a feature like this. They include computing the mean, standard deviation, minimum and maximum. There is a function in pandas for giving some basic summary stats of this type: adult["Hours-per-week"].describe() The result tells us a little about this feature. count 32561.000000 mean 40.437456 std 12.347429 min 1.000000 25% 40.000000 50% 40.000000 75% 45.000000 max 99.000000 dtype: float64 Some of these operations do not make sense for other features. For example, it doesn't make sense to compute the sum of the education statuses. There are also features that are not numerical, but still ordinal. The Education feature in the Adult dataset is an example of this. For example, a Bachelor's degree is a higher education status than finishing high school, which is a higher status than not completing high school. It doesn't quite make sense to compute the mean of these values, but we can create an approximation by taking the median value. The dataset gives a helpful feature Education-Num, which assigns a number that is basically equivalent to the number of years of education completed. This allows us to quickly compute the median: adult["Education-Num"].median() [ 85 ]
Extracting Features with Transformers The result is 10, or finishing one year past high school. If we didn't have this, we could compute the median by creating an ordering over the education values. Features can also be categorical. For instance, a ball can be a tennis ball, cricket ball, football, or any other type of ball. Categorical features are also referred to as nominal features. For nominal features, the values are either the same or they are different. While we could rank balls by size or weight, just the category alone isn't enough to compare things. A tennis ball is not a cricket ball, and it is also not a football. We could argue that a tennis ball is more similar to a cricket ball (say, in size), but the category alone doesn't differentiate this—they are the same, or they are not. We can convert categorical features to numerical features using the one-hot encoding, as we saw in Chapter 3, Predicting Sports Winners with Decision Trees. For the aforementioned categories of balls, we can create three new binary features: is a tennis ball, is a cricket ball, and is a football. For a tennis ball, the vector would be [1, 0, 0]. A cricket ball has the values [0, 1, 0], while a football has the values [0, 0, 1]. These features are binary, but can be used as continuous features by many algorithms. One key reason for doing this is that it easily allows for direct numerical comparison (such as computing the distance between samples). The Adult dataset contains several categorical features, with Work-Class being one example. While we could argue that some values are of higher rank than others (for instance, a person with a job is likely to have a better income than a person without), it doesn't make sense for all values. For example, a person working for the state government is not more or less likely to have a higher income than someone working in the private sector. We can view the unique values for this feature in the dataset using the unique() function: adult["Work-Class"].unique() The result shows the unique values in this column: array([' State-gov', ' Self-emp-not-inc', ' Private', ' Federal-gov', ' Local-gov', ' ?', ' Self-emp-inc', ' Without-pay', ' Never-worked', nan], dtype=object) There are some missing values in the preceding dataset, but they won't affect our computations in this example. [ 86 ]
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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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Page 57 and 58: Classifying with scikit-learn Estim
Page 65 and 66: Predicting Sports Winners with Deci
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
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Page 110 and 111: Chapter 5 Similarly, we can convert
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Page 120 and 121: Chapter 5 The downside to transform
Page 122 and 123: Chapter 5 A transformer is akin to
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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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