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Classifying with function words Next, we import our classes. The only new thing here is the support vector machines, which we will cover in the next section (for now, just consider it a standard classification algorithm). We import the SVC class, an SVM for classification, as well as the other standard workflow tools we have seen before: from sklearn.svm import SVC from sklearn.cross_validation import cross_val_score from sklearn.pipeline import Pipeline from sklearn import grid_search Chapter 9 Support vector machines take a number of parameters. As I said, we will use one blindly here, before going into detail in the next section. We then use a dictionary to set which parameters we are going to search. For the kernel parameter, we will try linear and rbf. For C, we will try values of 1 and 10 (descriptions of these parameters are covered in the next section). We then create a grid search to search these parameters for the best choices: parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]} svr = SVC() grid = grid_search.GridSearchCV(svr, parameters) Gaussian kernels (such as rbf) only work for reasonably sized datasets, such as when the number of features is fewer than about 10,000. Next, we set up a pipeline that takes the feature extraction step using the CountVectorizer (only using function words), along with our grid search using SVM. The code is as follows: pipeline1 = Pipeline([('feature_extraction', extractor), ('clf', grid) ]) Next, we apply cross_val_score to get our cross validated score for this pipeline. The result is 0.811, which means we approximately get 80 percent of the predictions correct. For 7 authors, this is a good result! [ 195 ]
Authorship Attribution Support vector machines Support vector machines (SVMs) are classification algorithms based on a simple and intuitive idea. It performs classification between only two classes (although we can extend it to more classes). Suppose that our two classes can be separated by a line such that any points above the line belong to one class and any below the line belong to the other class. SVMs find this line and use it for prediction, much the same way as linear regression works. SVMs, however, find the best line for separating the dataset. In the following figure, we have three lines that separate the dataset: blue, black, and green. Which would you say is the best option? Intuitively, a person would normally choose the blue line as the best option, as this separates the data the most. That is, it has the maximum distance from any point in each class. Finding this line is an optimization problem, based on finding the lines of margin with the maximum distance between them. [ 196 ]
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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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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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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 196 and 197: Chapter 8 Then we iterate over our
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Page 200 and 201: Chapter 8 This code correctly predi
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Page 204 and 205: Chapter 8 However, it isn't very go
Page 206: Chapter 8 Summary In this chapter,
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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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Learning Python Data Visualization
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