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Chapter 5 # which we don't want to count link_count_in_code += len(link_match.findall(match_str)) links = link_match.findall(s) link_count = len(links) link_count -= link_count_in_code html_free_s = re.sub(" +", " ", tag_match.sub('', code_free_s)).replace("\n", "") link_free_s = html_free_s # remove links from text before counting words for anchor in anchors: if anchor.lower().startswith("http://"): link_free_s = link_free_s.replace(anchor,'') num_text_tokens = html_free_s.count(" ") return num_text_tokens, num_code_lines, link_count Looking at the following graphs, we can notice that the number of words in a post show higher variability: Training on the bigger feature space improves accuracy quite a bit: Mean(scores)=0.58300 Stddev(scores)=0.02216 [ 99 ]
Classification – Detecting Poor Answers But still, this would mean that we could classify roughly four out of the ten wrong answers. At least we are heading in the right direction. More features lead to higher accuracy, which leads us to adding more features. Therefore, let us extend the feature space with even more features: • AvgSentLen: This feature measures the average number of words in a sentence. Maybe there is a pattern that particularly good posts don't overload the reader's brain with very long sentences. • AvgWordLen: This feature is similar to AvgSentLen; it measures the average number of characters in the words of a post. • NumAllCaps: This feature measures the number of words that are written in uppercase, which is considered a bad style. • NumExclams: This feature measures the number of exclamation marks. The following charts show the value distributions for average sentences and word lengths as well as the number of uppercase words and exclamation marks: [ 100 ]
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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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Learning How to Classify with Real-
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Chapter 2 We are using Matplotlib;
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Chapter 2 The last few lines select
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Chapter 2 error = 0.0 for ei in ran
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Chapter 2 We can play around with t
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Chapter 2 Features and feature engi
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Chapter 2 In the preceding screensh
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Chapter 2 Binary and multiclass cla
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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
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Page 78 and 79: Chapter 3 Flat clustering divides t
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Page 86 and 87: Chapter 3 Position Similarity Excer
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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
Page 101 and 102: Topic Modeling Alternatively, we ca
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
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Page 140 and 141: Chapter 6 Similarly, we do this for
Page 142 and 143: Chapter 6 A quick look at the previ
Page 144 and 145: Chapter 6 To keep our experimentati
Page 146 and 147: Chapter 6 Y = np.zeros(Y.shape[0])
Page 148 and 149: Chapter 6 ° ° Experiment with whe
Page 150 and 151: Chapter 6 We have to be patient whe
Page 152 and 153: Chapter 6 First, we define a range
Page 154 and 155: Chapter 6 Determining the word type
Page 156 and 157: Chapter 6 Successfully cheating usi
Page 158 and 159: Chapter 6 Our first estimator Now w
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Page 162 and 163: 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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