Building Machine Learning Systems with Python - Richert, Coelho

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Chapter 3 If you have a clear picture of what kind of stop words you would want to remove, you can also pass a list of them. Setting stop_words to "english" will use a set of 318 English stop words. To find out which ones they are, you can use get_stop_ words(): >>> sorted(vectorizer.get_stop_words())[0:20] ['a', 'about', 'above', 'across', 'after', 'afterwards', 'again', 'against', 'all', 'almost', 'alone', 'along', 'already', 'also', 'although', 'always', 'am', 'among', 'amongst', 'amoungst'] The new word list is seven words lighter: [u'actually', u'capabilities', u'contains', u'data', u'databases', u'images', u'imaging', u'interesting', u'learning', u'machine', u'permanently', u'post', u'provide', u'safe', u'storage', u'store', u'stuff', u'toy'] Without stop words, we arrive at the following similarity measurement: === Post 0 with dist=1.41: This is a toy post about machine learning. Actually, it contains not much interesting stuff. === Post 1 with dist=0.86: Imaging databases provide storage capabilities. === Post 2 with dist=0.86: Most imaging databases safe images permanently. === Post 3 with dist=0.77: Imaging databases store data. === Post 4 with dist=0.77: Imaging databases store data. Imaging databases store data. Imaging databases store data. Best post is 3 with dist=0.77 Post 2 is now on par with Post 1. Overall, it has, however, not changed much as our posts are kept short for demonstration purposes. It will become vital when we look at real-world data. Stemming One thing is still missing. We count similar words in different variants as different words. Post 2, for instance, contains "imaging" and "images". It would make sense to count them together. After all, it is the same concept they are referring to. We need a function that reduces words to their specific word stem. Scikit does not contain a stemmer by default. With the Natural Language Toolkit (NLTK), we can download a free software toolkit, which provides a stemmer that we can easily plug into CountVectorizer. [ 57 ]
Clustering – Finding Related Posts Installing and using NLTK How to install NLTK on your operating system is described in detail at http://nltk.org/install.html. Basically, you will need to install the two packages NLTK and PyYAML. To check whether your installation was successful, open a Python interpreter and type the following: >>> import nltk You will find a very nice tutorial for NLTK in the book Python Text Processing with NLTK 2.0 Cookbook. To play a little bit with a stemmer, you can visit the accompanied web page http://textprocessing.com/demo/stem/. NLTK comes with different stemmers. This is necessary, because every language has a different set of rules for stemming. For English, we can take SnowballStemmer. >>> import nltk.stem >>> s= nltk.stem.SnowballStemmer('english') >>> s.stem("graphics") u'graphic' >>> s.stem("imaging") u'imag' >>> s.stem("image") u'imag' >>> s.stem("imagination")u'imagin' >>> s.stem("imagine") u'imagin' Note that stemming does not necessarily have to result into valid English words. It also works with verbs as follows: >>> s.stem("buys") u'buy' >>> s.stem("buying") u'buy' >>> s.stem("bought") u'bought' [ 58 ]
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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
Page 23 and 24: 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 74 and 75: Chapter 3 Extending the vectorizer
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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 and 94: Topic Modeling Sparsity means that
Page 95 and 96: Topic Modeling Although daunting at
Page 97 and 98: Topic Modeling … for tj,v in t:
Page 99 and 100: Topic Modeling Finally, we build th
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Classification - Detecting Poor Ans
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Classification II - Sentiment Analy
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Chapter 6 Getting to know the Bayes
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Using Naive Bayes to classify Given
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Chapter 6 This denotation "" leads
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Chapter 6 Similarly, we do this for
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Chapter 6 A quick look at the previ
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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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Building Machine Learning Systems with Python - Richert, Coelho

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