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Chapter 5 The downside to transforming data with PCA is that these features are often complex combinations of the other features. For example, the first feature of the preceding code starts with [-0.092, -0.995, -0.024], that is, multiply the first feature in the original dataset by -0.092, the second by -0.995, the third by -0.024. This feature has 1,558 values of this form, one for each of the original datasets (although many are zeros). Such features are indistinguishable by humans and it is hard to glean much relevant information from without a lot of experience working with them. Using PCA can result in models that not only approximate the original dataset, but can also improve the performance in classification tasks: clf = DecisionTreeClassifier(random_state=14) scores_reduced = cross_val_score(clf, Xd, y, scoring='accuracy') The resulting score is 0.9356, which is (slightly) higher than our original score using all of the original features. PCA won't always give a benefit like this, but it does more often than not. We are using PCA here to reduce the number of features in our dataset. As a general rule, you shouldn't use it to reduce overfitting in your data mining experiments. The reason for this is that PCA doesn't take classes into account. A better solution is to use regularization. An introduction, with code, is available at http://blog.datadive. net/selecting-good-features-part-ii-linear-modelsand-regularization/. Another advantage is that PCA allows you to plot datasets that you otherwise couldn't easily visualize. For example, we can plot the first two features returned by PCA. First, we tell IPython to display plots inline and import pyplot: %matplotlib inline from matplotlib import pyplot as plt Next, we get all of the distinct classes in our dataset (there are only two: is ad or not ad): classes = set(y) We also assign colors to each of these classes: colors = ['red', 'green'] We use zip to iterate over both lists at the same time: for cur_class, color in zip(classes, colors): [ 97 ]
Extracting Features with Transformers We then create a mask of all of the samples that belong to the current class: mask = (y == cur_class).values Finally, we use the scatter function in pyplot to show where these are. We use the first two features as our x and y values in this plot. plt.scatter(Xd[mask,0], Xd[mask,1], marker='o', color=color, label=int(cur_class)) Finally, outside the loop, we create a legend and show the graph, showing where the samples from each class appear: plt.legend() plt.show() Creating your own transformer As the complexity and type of dataset changes, you might find that you can't find an existing feature extraction transformer that fits your needs. We will see an example of this in Chapter 7, Discovering Accounts to Follow Using Graph Mining, where we create new features from graphs. [ 98 ]
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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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Predicting Sports Winners with Deci
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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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Learning Python Data Visualization
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