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Chapter 2 Pipelines take a list of steps as input, representing the chain of the data mining application. The last step needs to be an Estimator, while all previous steps are Transformers. The input dataset is altered by each Transformer, with the output of one step being the input of the next step. Finally, the samples are classified by the last step's estimator. In our pipeline, we have two steps: 1. Use MinMaxScaler to scale the feature values from 0 to 1 2. Use KNeighborsClassifier as the classification algorithms Each step is then represented by a tuple ('name', step). We can then create our pipeline: scaling_pipeline = Pipeline([('scale', MinMaxScaler()), ('predict', KNeighborsClassifier())]) The key here is the list of tuples. The first tuple is our scaling step and the second tuple is the predicting step. We give each step a name: the first we call scale and the second we call predict, but you can choose your own names. The second part of the tuple is the actual Transformer or estimator object. Running this pipeline is now very easy, using the cross validation code from before: scores = cross_val_score(scaling_pipeline, X_broken, y, scoring='accuracy') print("The pipeline scored an average accuracy for is {0:.1f}%". format(np.mean(transformed_scores) * 100)) This gives us the same score as before (82.3 percent), which is expected, as we are effectively running the same steps. In later chapters, we will use more advanced testing methods, and setting up pipelines is a great way to ensure that the code <strong>com</strong>plexity does not grow unmanageably. [ 39 ]
Classifying with scikit-learn Estimators Summary In this chapter, we used several of scikit-learn's methods for building a standard workflow to run and evaluate data mining models. We introduced the Nearest Neighbors algorithm, which is already implemented in scikit-learn as an estimator. Using this class is quite easy; first, we call the fit function on our training data, and second, we use the predict function to predict the class of testing samples. We then looked at preprocessing by fixing poor feature scaling. This was done using a Transformer object and the MinMaxScaler class. These functions also have a fit method and then a transform, which takes a dataset as an input and returns a transformed dataset as an output. In the next chapter, we will use these concepts in a larger example, predicting the out<strong>com</strong>e of sports matches using real-world data. [ 40 ]
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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
Page 11 and 12: Table of Contents Preprocessing usi
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Page 15 and 16: Table of Contents GPU optimization
Page 18 and 19: Preface If you have ever wanted to
Page 20 and 21: What you need for this book It shou
Page 22 and 23: Preface Reader feedback Feedback fr
Page 24 and 25: Getting Started with Data Mining We
Page 26 and 27: Chapter 1 In the preceding dataset,
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Page 32 and 33: Chapter 1 The dataset we are going
Page 34 and 35: Chapter 1 As an example, we will co
Page 36 and 37: We get the names of the features fo
Page 38 and 39: Chapter 1 Two rules are near the to
Page 40 and 41: Chapter 1 The scikit-learn library
Page 42 and 43: We then iterate over all the sample
Page 44 and 45: Chapter 1 Overfitting is the proble
Page 46: Chapter 1 Summary In this chapter,
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Page 84 and 85: Recommending Movies Using Affinity
Page 86 and 87: Chapter 4 The classic algorithm for
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Page 92 and 93: Chapter 4 Implementation On the fir
Page 94 and 95: Chapter 4 We want to break out the
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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 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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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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