Building Machine Learning Systems with Python - Richert, Coelho

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Chapter 6 Getting to know the Bayes theorem At its core, Naive Bayes classification is nothing more than keeping track of which feature gives evidence to which class. To ease our understanding, let us assume the following meanings for the variables that we will use to explain Naive Bayes: Variable Possible values Meaning "pos", "neg" Class of a tweet (positive or negative) Non-negative integers Non-negative integers Counting the occurrence of awesome in the tweet Counting the occurrence of crazy in the tweet During training, we learn the Naive Bayes model, which is the probability for a class when we already know features and . This probability is written as . Since we cannot estimate this directly, we apply a trick, which was found out by Bayes: If we substitute with the probability of both features and occurring and think of as being our class , we arrive at the relationship that helps us to later retrieve the probability for the data instance belonging to the specified class: This allows us to express by means of the other probabilities: We could also say that: [ 119 ]
Classification II – Sentiment Analysis The prior and evidence values are easily determined: • is the prior probability of class without knowing about the data. This quantity can be obtained by simply calculating the fraction of all training data instances belonging to that particular class. • is the evidence, or the probability of features and . This can be retrieved by calculating the fraction of all training data instances having that particular feature value. • The tricky part is the calculation of the likelihood . It is the value describing how likely it is to see feature values and if we know that the class of the data instance is . To estimate this we need a bit more thinking. Being naive From the probability theory, we also know the following relationship: This alone, however, does not help much, since we treat one difficult problem (estimating ) with another one (estimating ). However, if we naively assume that and are independent from each other, simplifies to and we can write it as follows: Putting everything together, we get this quite manageable formula: The interesting thing is that although it is not theoretically correct to simply tweak our assumptions when we are in the mood to do so, in this case it proves to work astonishingly well in real-world applications. [ 120 ]
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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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Clustering - Finding Related Posts
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Chapter 3 How to do it More robust
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Chapter 3 This means that the first
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Chapter 3 ... post = posts[i] ... i
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Chapter 3 If you have a clear pictu
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Chapter 3 Extending the vectorizer
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Chapter 3 0.0 >>> print(tfidf("b",
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Chapter 3 Flat clustering divides t
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Because the cluster centers are mov
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Chapter 3 'D:\\data\\379\\raw\\comp
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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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Page 103 and 104: Topic Modeling Topic modeling was f
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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
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
Page 160 and 161: Chapter 6 for d in documents: allca
Page 162 and 163: Regression - Recommendations You ha
Page 164 and 165: Chapter 7 The preceding graph shows
Page 166 and 167: Chapter 7 Root mean squared error a
Page 168 and 169: Penalized regression The important
Page 170 and 171: Chapter 7 P greater than N scenario
Page 172 and 173: Chapter 7 So, we can see that the d
Page 174 and 175: Chapter 7 Fortunately, scikit-learn
Page 176 and 177: [ 161 ] Chapter 7 The loading of th
Page 178: Chapter 7 Summary In this chapter,
Page 181 and 182: Regression - Recommendations Improv
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Weights Regression - Recommendation
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Regression - Recommendations Improv
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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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