Building Machine Learning Systems with Python - Richert, Coelho

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Chapter 4 Now we can see that many documents touch upon 20 to 25 different topics. What are these topics? Technically, they are multinomial distributions over words, which mean that they give each word in the vocabulary a probability. Words with high probability are more associated with that topic than words with lower probability. Our brains aren't very good at reasoning with probability distributions, but we can readily make sense of a list of words. Therefore, it is typical to summarize topics with a the list of the most highly weighted words. Here are the first ten topics: • dress military soviet president new state capt carlucci states leader stance government • koch zambia lusaka one-party orange kochs party i government mayor new political • human turkey rights abuses royal thompson threats new state wrote garden president • bill employees experiments levin taxation federal measure legislation senate president whistleblowers sponsor • ohio july drought jesus disaster percent hartford mississippi crops northern valley virginia • united percent billion year president world years states people i bush news • b hughes affidavit states united ounces squarefoot care delaying charged unrealistic bush • yeutter dukakis bush convention farm subsidies uruguay percent secretary general i told • Kashmir government people srinagar india dumps city two jammu-kashmir group moslem pakistan • workers vietnamese irish wage immigrants percent bargaining last island police hutton I [ 79 ]
Topic Modeling Although daunting at first glance, we can clearly see that the topics are not just random words, but are connected. We can also see that these topics refer to older news items, from when the Soviet union still existed and Gorbachev was its Secretary General. We can also represent the topics as word clouds, making more likely words larger For example, this is the visualization of a topic, which deals with the Middle East and politics: We can also see that some of the words should perhaps be removed (for example, the word I) as they are not so informative (stop words). In topic modeling, it is important to filter out stop words, as otherwise you might end up with a topic consisting entirely of stop words, which is not very informative. We may also wish to preprocess the text to stems in order to normalize plurals and verb forms. This process was covered in the previous chapter, and you can refer to it for details. If you are interested, you can download the code from the companion website of the book and try all these variations to draw different pictures. Building a word cloud like the one in the previous screenshot can be done with several different pieces of software. For the previous graphic, I used the online tool wordle (http://www.wordle.net), which generates particularly attractive images. Since I only had a few examples, I copy and pasted the list of words manually, but it is possible to use it as a web service and call it directly from Python. Comparing similarity in topic space Topics can be useful on their own to build small vignettes with words that are in the previous screenshot. These visualizations could be used to navigate a large collection of documents and, in fact, they have been used in just this way. [ 80 ]
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Building Machine Learning Systems w
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Credits Authors Willi Richert Luis
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Luis Pedro Coelho is a Computationa
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Maurice HT Ling completed his PhD.
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Table of Contents Preface 1 Chapter
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Table of Contents Tuning the instan
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Table of Contents Improving classif
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Preface You could argue that it is
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Preface What you need for this book
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Downloading the example code You ca
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Getting Started with Python Machine
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Page 43 and 44: Getting Started with Python Machine
Page 45 and 46: Getting Started with Python Machine
Page 48 and 49: Learning How to Classify with Real-
Page 50 and 51: Chapter 2 We are using Matplotlib;
Page 52 and 53: Chapter 2 The last few lines select
Page 54 and 55: Chapter 2 error = 0.0 for ei in ran
Page 56 and 57: Chapter 2 We can play around with t
Page 58 and 59: Chapter 2 Features and feature engi
Page 60 and 61: Chapter 2 In the preceding screensh
Page 62 and 63: Chapter 2 Binary and multiclass cla
Page 64 and 65: Clustering - Finding Related Posts
Page 66 and 67: Chapter 3 How to do it More robust
Page 68 and 69: Chapter 3 This means that the first
Page 70 and 71: Chapter 3 ... post = posts[i] ... i
Page 72 and 73: Chapter 3 If you have a clear pictu
Page 74 and 75: Chapter 3 Extending the vectorizer
Page 76 and 77: Chapter 3 0.0 >>> print(tfidf("b",
Page 78 and 79: Chapter 3 Flat clustering divides t
Page 80 and 81: Because the cluster centers are mov
Page 82 and 83: Chapter 3 'D:\\data\\379\\raw\\comp
Page 84 and 85: As we have learned previously, we w
Page 86 and 87: Chapter 3 Position Similarity Excer
Page 88: Chapter 3 But before you go there,
Page 91 and 92: Topic Modeling For those who are in
Page 93: Topic Modeling Sparsity means that
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Page 99 and 100: Topic Modeling Finally, we build th
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Page 103 and 104: Topic Modeling Topic modeling was f
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Page 134 and 135: Chapter 6 Getting to know the Bayes
Page 136 and 137: Using Naive Bayes to classify Given
Page 138 and 139: Chapter 6 This denotation "" leads
Page 140 and 141: Chapter 6 Similarly, we do this for
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Chapter 6 To keep our experimentati
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Chapter 6 Y = np.zeros(Y.shape[0])
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Chapter 6 ° ° Experiment with whe
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Chapter 6 We have to be patient whe
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Chapter 6 First, we define a range
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Chapter 6 Determining the word type
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Chapter 6 Successfully cheating usi
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Chapter 6 Our first estimator Now w
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Chapter 6 for d in documents: allca
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Regression - Recommendations You ha
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Chapter 7 The preceding graph shows
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Chapter 7 Root mean squared error a
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Penalized regression The important
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Chapter 7 P greater than N scenario
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Chapter 7 So, we can see that the d
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Chapter 7 Fortunately, scikit-learn
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[ 161 ] Chapter 7 The loading of th
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Chapter 7 Summary In this chapter,
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Regression - Recommendations Improv
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Weights Regression - Recommendation
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Classification III - Music Genre Cl
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Chapter 9 Matplotlib provides the c
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[ 185 ] Chapter 9
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Chapter 9 def create_fft(fn): sampl
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Chapter 9 ax.set_yticks(range(len(g
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Chapter 9 On the left-hand side gra
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[ 193 ] Chapter 9 Improving classif
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We get the following promising resu
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Chapter 9 Summary In this chapter,
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Computer Vision - Pattern Recogniti
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Dimensionality Reduction Sketching
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Dimensionality Reduction However, t
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Dimensionality Reduction To underst
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Dimensionality Reduction In order t
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Dimensionality Reduction Asking the
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Dimensionality Reduction n_ feature
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Dimensionality Reduction Sketching
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Dimensionality Reduction Limitation
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Dimensionality Reduction Now, MDS t
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Dimensionality Reduction Of course,
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Big(ger) Data • Your algorithms c
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Big(ger) Data sleep(4) return 2*x @
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Big(ger) Data Looking under the hoo
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Big(ger) Data def write_result(ofna
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Big(ger) Data Amazon Web Services i
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Big(ger) Data In EC2 parlance, a ru
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Big(ger) Data In this system, pip i
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Big(ger) Data Keys, keys, and more
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Big(ger) Data We can use the same j
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Where to Learn More about Machine L
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• Machined Learnings at http://ww
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Index A AcceptedAnswerId attribute
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F false negative 41 false positive
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inary matrix of recommendations, us
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sklearn.naive_bayes package 127 skl
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NumPy Beginner's Guide - Second Edi
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