Learning Data Mining with Python

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Appendix To install it, clone the repository and follow the instructions to install the Bleeding Edge code available at: http://scikit-learn.org/stable/install.html. Remember to use the above repository's code, rather than the official source. I recommend you install using virtualenv or a virtual machine, rather than installing it directly on your computer. A great guide to virtualenv can be found here: http://docs.python-guide.org/en/latest/dev/virtualenvs/. More complex pipelines http://scikit-learn.org/stable/modules/pipeline.html#featureunioncomposite-feature-spaces The Pipelines we have used in the book follow a single stream—the output of one step is the input of another step. Pipelines follow the transformer and estimator interfaces as well—this allows us to embed Pipelines within Pipelines. This is a useful construct for very complex models, but becomes very powerful when combined with Feature Unions, as shown in the above link. This allows us to extract multiple types of features at a time and then combine them to form a single dataset. For more details, see the example at: http://scikitlearn.org/stable/auto_examples/feature_stacker.html. Comparing classifiers There are lots of classifiers in scikit-learn that are ready to use. The one you choose for a particular task is going to be based on a variety of factors. You can compare the f1-score to see which method is better, and you can investigate the deviation of those scores to see if that result is statistically significant. An important factor is that they are trained and tested on the same data—that is, the test set for one classifier is the test set for all classifiers. Our use of random states allows us to ensure this is the case—an important factor for replicating experiments. [ 299 ]
Next Steps… Chapter 3: Predicting Sports Winners with Decision Trees More on pandas http://pandas.pydata.org/pandas-docs/stable/tutorials.html The pandas library is a great package—anything you normally write to do data loading is probably already implemented in pandas. You can learn more about it from their tutorial, linked above. There is also a great blog post written by Chris Moffitt that overviews common tasks people do in Excel and how to do them in pandas: http://pbpython.com/excelpandas-comp.html You can also handle large datasets with pandas; see the answer, from user Jeff (the top answer at the time of writing), to this StackOverflow question for an extensive overview of the process: http://stackoverflow.com/questions/14262433/ large-data-work-flows-using-pandas. Another great tutorial on pandas is written by Brian Connelly: http://bconnelly.net/2013/10/summarizing-data-in-python-with-pandas/ More complex features http://www.basketball-reference.com/teams/ORL/2014_roster_status.html Sports teams change regularly from game to game. What is an easy win for a team can turn into a difficult game if a couple of the best players are injured. You can get the team rosters from basketball-reference as well. For example, the roster for the 2013-2014 season for the Orlando Magic is available at the above link—similar data is available for all NBA teams. Writing code to integrate how much a team changes, and using that to add new features, can improve the model significantly. This task will take quite a bit of work, though! [ 300 ]
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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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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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Chapter 8 Summary In this chapter,
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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 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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