Building Machine Learning Systems with Python - Richert, Coelho

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Chapter 3 But before you go there, you will have to define what you actually mean by "better". Scikit has a complete package dedicated only to this definition. The package is called sklearn.metrics and also contains a full range of different metrics to measure clustering quality. Maybe that should be the first place to go now, right into the sources of the metrics package. Summary That was a tough ride, from preprocessing over clustering to a solution that can convert noisy text into a meaningful concise vector representation that we can cluster. If we look at the efforts we had to do to finally be able to cluster, it was more than half of the overall task, but on the way, we learned quite a bit on text processing and how simple counting can get you very far in the noisy real-world data. The ride has been made much smoother though, because of Scikit and its powerful packages. And there is more to explore. In this chapter we were scratching the surface of its capabilities. In the next chapters we will see more of its powers. [ 73 ]
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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 37 and 38: 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 90 and 91: Topic Modeling In the previous chap
Page 92 and 93: Chapter 4 Corpus is just the preloa
Page 94 and 95: Chapter 4 Now we can see that many
Page 96 and 97: Chapter 4 However, topics are often
Page 98 and 99: Chapter 4 Subject: Re: High Prolact
Page 100 and 101: We can also ask what the most talke
Page 102 and 103: Chapter 4 If you are going to explo
Page 104 and 105: Classification - Detecting Poor Ans
Page 106 and 107: Chapter 5 Fetching the data Luckily
Page 108 and 109: Preselection and processing of attr
Page 110 and 111: Chapter 5 Creating our first classi
Page 112 and 113: Chapter 5 With the majority of post
Page 114 and 115: Chapter 5 # which we don't want to
Page 116 and 117: With these four additional features
Page 118 and 119: The only possibilities we have in t
Page 120 and 121: Chapter 5 But this is not enough, a
Page 122 and 123: Chapter 5 This means that we can no
Page 124 and 125: Chapter 5 Method mean(scores) stdde
Page 126 and 127: If instead our goal would have been
Page 128 and 129: Chapter 5 Setting the threshold at
Page 130: Chapter 5 Ship it! Let's assume we
Page 133 and 134: Classification II - Sentiment Analy
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Classification II - Sentiment Analy
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Regression - Recommendations We sta
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Regression - Recommendations In the
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Regression - Recommendations The Li
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Regression - Recommendations Ridge,
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Regression - Recommendations Howeve
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Regression - Recommendations Settin
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Regression - Recommendations Unfort
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Regression - Recommendations reg.fi
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Regression - Recommendations Improv
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We are now going to use this binary
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Chapter 8 Now, we iterate over all
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coefficients = [] # We are now goin
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Chapter 8 The beer and diapers stor
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Chapter 8 Formally, Apriori takes a
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Chapter 8 Refer to the following co
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Chapter 8 Summary In this chapter,
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Classification III - Music Genre Cl
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Computer Vision - Pattern Recogniti
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Chapter 10 However, some specialize
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Chapter 10 Instead of rgb2gray, we
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Chapter 10 This is still not perfec
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Filtering for different effects The
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Chapter 10 Now we filter the 3 chan
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Chapter 10 We previously used an ex
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Chapter 10 However, it is also poss
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Feature sets may be combined easily
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The descriptors_only=True flag mean
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Chapter 10 The result is that each
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Dimensionality Reduction Garbage in
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Chapter 11 Detecting redundant feat
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Chapter 11 Although the human eye i
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Chapter 11 We can see that this sit
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Chapter 11 Hence, we have to calcul
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Chapter 11 Coming back to Scikit-le
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Chapter 11 Feature extraction At so
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Chapter 11 Scikit-learn provides th
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Chapter 11 That's all. Note that in
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Chapter 11 Let us have a look at a
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Big(ger) Data While computers keep
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Chapter 12 A task could be "call do
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Chapter 12 Now we run jug status ag
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Chapter 12 Jug is also specially op
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Chapter 12 There are three modes of
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We pick and click on EC2 (the secon
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Chapter 12 Therefore, we will be ca
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Chapter 12 You can assign a fixed I
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Chapter 12 We need to create a new
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Chapter 12 Summary We saw how to us
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Where to Learn More about Machine L
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Where to Learn More about Machine L
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classification model building 35, 3
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jug execute file 243 jugfile.jugdat
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limitations 236 sketching 234 pears
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Thank you for buying Building Machi
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Instant Pygame for Python Game Deve
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