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Classifying with scikit-learn Estimators The scikit-learn library is a collection of data mining algorithms, written in Python and using a <strong>com</strong>mon programming interface. This allows users to easily try different algorithms as well as utilize standard tools for doing effective testing and parameter searching. There are a large number of algorithms and utilities in scikit-learn. In this chapter, we focus on setting up a good framework for running data mining procedures. This will be used in later chapters, which are all focused on applications and techniques to use in those situations. The key concepts introduced in this chapter are as follows: • Estimators: This is to perform classification, clustering, and regression • Transformers: This is to perform preprocessing and data alterations • Pipelines: This is to put together your workflow into a replicable format scikit-learn estimators Estimators are scikit-learn's abstraction, allowing for the standardized implementation of a large number of classification algorithms. Estimators are used for classification. Estimators have the following two main functions: • fit(): This performs the training of the algorithm and sets internal parameters. It takes two inputs, the training sample dataset and the corresponding classes for those samples. • predict(): This predicts the class of the testing samples that is given as input. This function returns an array with the predictions of each input testing sample. [ 25 ]
Classifying with scikit-learn Estimators Most scikit-learn estimators use the NumPy arrays or a related format for input and output. There are a large number of estimators in scikit-learn. These include support vector machines (SVM), random forests, and neural networks. Many of these algorithms will be used in later chapters. In this chapter, we will use a different estimator from scikit-learn: nearest neighbor. For this chapter, you will need to install a new library called matplotlib. The easiest way to install it is to use pip3, as you did in Chapter 1, Getting Started with Data Mining, to install scikit-learn: $pip3 install matplotlib If you have any difficulty installing matplotlib, seek the official installation instructions at http://matplotlib.org/users/ installing.html. Nearest neighbors Nearest neighbors is perhaps one of the most intuitive algorithms in the set of standard data mining algorithms. To predict the class of a new sample, we look through the training dataset for the samples that are most similar to our new sample. We take the most similar sample and predict the class that the majority of those samples have. As an example, we wish to predict the class of the triangle, based on which class it is more similar to (represented here by having similar objects closer together). We seek the three nearest neighbors, which are two diamonds and one square. There are more diamonds than circles, and the predicted class for the triangle is, therefore, a diamond: [ 26 ]
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Page 11 and 12: Table of Contents Preprocessing usi
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Page 18 and 19: Preface If you have ever wanted to
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movie_name_data.columns = ["MovieID
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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 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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Discovering Accounts to Follow Usin
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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 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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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 We then print out the mo
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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 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 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 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 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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