Building Machine Learning Systems with Python - Richert, Coelho

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Chapter 11 We can see that this situation is less uncertain. The uncertainty will decrease the farther we get from 0.5, reaching the extreme value of 0 for either a 0 percent or 100 percent chance of the head showing up, as we can see in the following graph: We will now modify the entropy by applying it to two features instead of one, such that it measures how much uncertainty is removed from X when we learn about Y. Then we can catch how one feature reduces the uncertainty of another. For example, <strong>with</strong>out having any further information about the weather, we are totally uncertain whether it is raining outside or not. If we now learn that the grass outside is wet, the uncertainty has been reduced (we will still have to check whether the sprinkler had been turned on). More formally, mutual information is defined as: This looks a bit intimidating, but is really not more than sums and products. For instance, the calculation of is done by binning the feature values and then calculating the fraction of values in each bin. In the following plots, we have set the number of bins to 10. [ 227 ]
Dimensionality Reduction In order to restrict mutual information to the interval [0,1], we have to divide it by their added individual entropy, which gives us the normalized mutual information: The nice thing about mutual information is that unlike correlation, it is not looking only at linear relationships, as we can see in the following graphs: [ 228 ]
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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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As we have learned previously, we w
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Chapter 3 Position Similarity Excer
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Chapter 3 But before you go there,
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Topic Modeling For those who are in
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Topic Modeling Sparsity means that
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Topic Modeling Although daunting at
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Topic Modeling … for tj,v in t:
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Topic Modeling Finally, we build th
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Topic Modeling Alternatively, we ca
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Topic Modeling Topic modeling was f
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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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Page 278 and 279: • Machined Learnings at http://ww
Page 280 and 281: Index A AcceptedAnswerId attribute
Page 282 and 283: F false negative 41 false positive
Page 284 and 285: inary matrix of recommendations, us
Page 286: sklearn.naive_bayes package 127 skl
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