Learning Data Mining with Python

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Chapter 5 [18, 19, 20], [21, 22, 23], [24, 25, 26], [27, 28, 29]]) Then, we set the entire second column/feature to the value 1: X[:,1] = 1 The result has lots of variance in the first and third rows, but no variance in the second row: array([[ 0, 1, 2], [ 3, 1, 5], [ 6, 1, 8], [ 9, 1, 11], [12, 1, 14], [15, 1, 17], [18, 1, 20], [21, 1, 23], [24, 1, 26], [27, 1, 29]]) We can now create a VarianceThreshold transformer and apply it to our dataset: from sklearn.feature_selection import VarianceThreshold vt = VarianceThreshold() Xt = vt.fit_transform(X) Now, the result Xt does not have the second column: array([[ 0, 2], [ 3, 5], [ 6, 8], [ 9, 11], [12, 14], [15, 17], [18, 20], [21, 23], [24, 26], [27, 29]]) We can observe the variances for each column by printing the vt.variances_ attribute: print(vt.variances_) [ 89 ]
Extracting Features with Transformers The result shows that while the first and third column contains at least some information, the second column had no variance: array([ 74.25, 0. , 74.25]) A simple and obvious test like this is always good to run when seeing data for the first time. Features with no variance do not add any value to a data mining application; however, they can slow down the performance of the algorithm. Selecting the best individual features If we have a number of features, the problem of finding the best subset is a difficult task. It relates to solving the data mining problem itself, multiple times. As we saw in Chapter 4, Recommending Movies Using Affinity Analysis, subset-based tasks increase exponentially as the number of features increase. This exponential growth in time needed is also true for finding the best subset of features. A workaround to this problem is not to look for a subset that works well together, rather than just finding the best individual features. This univariate feature selection gives us a score based on how well a feature performs by itself. This is usually done for classification tasks, and we generally measure some type of correlation between a variable and the target class. The scikit-learn package has a number of transformers for performing univariate feature selection. They include SelectKBest, which returns the k best performing features, and SelectPercentile, which returns the top r% of features. In both cases, there are a number of methods of computing the quality of a feature. There are many different methods to compute how effectively a single feature correlates with a class value. A commonly used method is the chi-squared (χ2) test. Other methods include mutual information and entropy. We can observe single-feature tests in action using our Adult dataset. First, we extract a dataset and class values from our pandas DataFrame. We get a selection of the features: X = adult[["Age", "Education-Num", "Capital-gain", "Capital-loss", "Hours-per-week"]].values We will also create a target class array by testing whether the Earnings-Raw value is above $50,000 or not. If it is, the class will be True. Otherwise, it will be False. Let's look at the code: y = (adult["Earnings-Raw"] == ' >50K').values [ 90 ]
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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 61 and 62: Classifying with scikit-learn Estim
Page 63 and 64: Classifying with scikit-learn Estim
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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
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Page 104 and 105: Extracting Features with Transforme
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Page 108 and 109: Chapter 5 Other features describe a
Page 110 and 111: Chapter 5 Similarly, we can convert
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
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Page 126: Chapter 5 Putting it all together N
Page 129 and 130: Social Media Insight Using Naive Ba
Page 158 and 159: Discovering Accounts to Follow Usin
Page 160 and 161: 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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