Building Machine Learning Systems with Python - Richert, Coelho

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Chapter 1 Although we cannot look into the future, we can and should simulate a similar effect by holding out a part of our data. Let us remove, for instance, a certain percentage of the data and train on the remaining one. Then we use the hold-out data to calculate the error. As the model has been trained not knowing the hold-out data, we should get a more realistic picture of how the model will behave in the future. The test errors for the models trained only on the time after the inflection point now show a completely different picture. Error d=1: 7,917,335.831122 Error d=2: 6,993,880.348870 Error d=3: 7,137,471.177363 Error d=10: 8,805,551.189738 Error d=100: 10,877,646.621984 The result can be seen in the following chart: It seems we finally have a clear winner. The model with degree 2 has the lowest test error, which is the error when measured using data that the model did not see during training. And this is what lets us trust that we won't get bad surprises when future data arrives. [ 29 ]
Getting Started with Python Machine Learning Answering our initial question Finally, we have arrived at a model that we think represents the underlying process best; it is now a simple task of finding out when our infrastructure will reach 100,000 requests per hour. We have to calculate when our model function reaches the value 100,000. Having a polynomial of degree 2, we could simply compute the inverse of the function and calculate its value at 100,000. Of course, we would like to have an approach that is applicable to any model function easily. This can be done by subtracting 100,000 from the polynomial, which results in another polynomial, and finding the root of it. SciPy's optimize module has the fsolve function to achieve this when providing an initial starting position. Let fbt2 be the winning polynomial of degree 2: >>> print(fbt2) 2 0.08844 x - 97.31 x + 2.853e+04 >>> print(fbt2-100000) 2 0.08844 x - 97.31 x - 7.147e+04 >>> from scipy.optimize import fsolve >>> reached_max = fsolve(fbt2-100000, 800)/(7*24) >>> print("100,000 hits/hour expected at week %f" % reached_max[0]) 100,000 hits/hour expected at week 9.827613 Our model tells us that given the current user behavior and traction of our startup, it will take another month until we have reached our threshold capacity. Of course, there is a certain uncertainty involved with our prediction. To get the real picture, you can draw in more sophisticated statistics to find out about the variance that we have to expect when looking farther and further into the future. And then there are the user and underlying user behavior dynamics that we cannot model accurately. However, at this point we are fine with the current predictions. After all, we can prepare all the time-consuming actions now. If we then monitor our web traffic closely, we will see in time when we have to allocate new resources. [ 30 ]
Page 2 and 3: Building Machine Learning Systems w
Page 4 and 5: Credits Authors Willi Richert Luis
Page 6 and 7: Luis Pedro Coelho is a Computationa
Page 8 and 9: Maurice HT Ling completed his PhD.
Page 10 and 11: Table of Contents Preface 1 Chapter
Page 12 and 13: Table of Contents Tuning the instan
Page 14 and 15: Table of Contents Improving classif
Page 16 and 17: Preface You could argue that it is
Page 18 and 19: Preface What you need for this book
Page 20: Downloading the example code You ca
Page 23 and 24: Getting Started with Python Machine
Page 43: 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
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Page 91 and 92: Topic Modeling For those who are in
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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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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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