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Chapter 5 This means that we can now fit linear combinations of our features (ok, we have only one feature and a constant, but that will change soon) to the values. Let's consider the linear equation in Chapter 1, Getting Started with Python Machine Learning shown as follows: This can be replaced with the following equation (by replacing y with p): We can solve the equation for as shown in the following formula: We simply have to find the right coefficients such that the formula will give the lowest errors for all our pairs (xi, pi) in the dataset, which will be detected by Scikit-learn. After fitting the data to the class labels, the formula will give the probability for every new data point, x, that belongs to class 1. Refer to the following code: >>> from sklearn.linear_model import LogisticRegression >>> clf = LogisticRegression() >>> print(clf) LogisticRegression(C=1.0, class_weight=None, dual=False, fit_ intercept=True, intercept_scaling=1, penalty=l2, tol=0.0001) [ 107 ]
Classification – Detecting Poor Answers >>> clf.fit(X, y) >>> print(np.exp(clf.intercept_), np.exp(clf.coef_.ravel())) [ 0.09437188] [ 1.80094112] >>> def lr_model(clf, X): return 1 / (1 + np.exp(-(clf.intercept_ + clf.coef_*X))) >>> print("P(x=-1)=%.2f\tP(x=7)=%.2f"%(lr_model(clf, -1), lr_ model(clf, 7))) P(x=-1)=0.05 P(x=7)=0.85 You might have noticed that Scikit-learn exposes the first coefficient through the special field intercept_. If we plot the fitted model, we see that it makes perfect sense given the data: Applying logistic regression to our postclassification problem Admittedly, the example in the previous section was created to show the beauty of logistic regression. How does it perform on the extremely noisy data? Comparing it to the best nearest neighbour classifier (k = 90) as a baseline, we see that it performs a bit better, but also won't change the situation a whole lot: Method mean(scores) stddev(scores) LogReg C=0.1 0.6310 0.02791 LogReg C=100.00 0.6300 0.03170 LogReg C=10.00 0.6300 0.03170 LogReg C=0.01 0.6295 0.02752 [ 108 ]
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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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Page 74 and 75: Chapter 3 Extending the vectorizer
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Page 88: Chapter 3 But before you go there,
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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
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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
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
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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
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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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