Building Machine Learning Systems with Python - Richert, Coelho

More documents

Recommendations

Info

Chapter 9 On the left-hand side graph, we see the P/R curve. For an ideal classifier, we would have the curve going from the top-left corner directly to the top-right corner and then to the bottom-right corner, resulting in an area under curve (AUC) of 1.0. The right-hand side graph depicts the corresponding ROC curve. It plots the true positive rate over the false positive rate. Here, an ideal classifier would have a curve going from the lower-left to top-left corner and then to the top-right corner. A random classifier would be a straight line from the lower-left to upper-right corner, as shown by the dashed line having an AUC of 0.5. Therefore, we cannot compare the AUC of a P/R curve <strong>with</strong> that of an ROC curve. When comparing two different classifiers on the same dataset, we are always safe to assume that a higher AUC of a P/R curve for one classifier also means a higher AUC of the corresponding ROC curve and vice versa. Therefore, we never bother to generate both. More on this can be found in the very insightful paper The Relationship Between Precision-Recall and ROC Curves, Jesse Davis and Mark Goadrich, ICML 2006. The definitions of both the curves' x and y axes are given in the following table: P/R x axis y axis ROC Looking at the definitions of both curves' x axes and y axes, we see that the true positive rate in the ROC curve's y axis is the same as Recall of the P/R graph's x axis. The false positive rate measures the fraction of true negative examples that were falsely identified as positive ones, giving a 0 in a perfect case (no false positives) and 1 otherwise. Contrast this to the Precision curve, where we track exactly the opposite, namely the fraction of true positive examples that we correctly classified as such. Going forward, let us use ROC curves to measure our classifier's performance to get a better feeling for it. The only challenge for our multiclass problem is that both ROC and P/R curves assume a binary classification problem. For our purpose, let us therefore create one chart per genre that shows how the classifier performed a "one versus rest" classification: y_pred = clf.predict(X_test) for label in labels: [ 191 ]
Classification III – Music Genre Classification y_label_test = np.asarray(y_test==label, dtype=int) proba = clf.predict_proba(X_test) proba_label = proba[:,label] fpr, tpr, roc_thresholds = roc_curve(y_label_test, proba_ label) # plot tpr over fpr # ... The outcome will be the six ROC plots shown in the following screenshot. As we have already found out, our first version of a classifier only performs well on classical songs. Looking at the individual ROC curves, however, tells us that we are really underperforming for most of the other genres. Only jazz and country provide some hope. The remaining genres are clearly not usable: [ 192 ]
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 25 and 26:
Page 27 and 28:
Page 29 and 30:
Page 31 and 32:
Page 33 and 34:
Page 35 and 36:
Page 37 and 38:
Page 39 and 40:
Page 41 and 42:
Page 43 and 44:
Page 45 and 46:
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
Page 80 and 81:
Because the cluster centers are mov
Page 82 and 83:
Chapter 3 'D:\\data\\379\\raw\\comp
Page 84 and 85:
As we have learned previously, we w
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
Page 101 and 102:
Topic Modeling Alternatively, we ca
Page 103 and 104:
Topic Modeling Topic modeling was f
Page 105 and 106:
Classification - Detecting Poor Ans
Page 107 and 108:
Page 109 and 110:
Page 111 and 112:
Page 113 and 114:
Page 115 and 116:
Page 117 and 118:
Page 119 and 120:
Page 121 and 122:
Page 123 and 124:
Page 125 and 126:
Page 127 and 128:
Page 129 and 130:
Page 132 and 133:
Classification II - Sentiment Analy
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
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
Page 185 and 186: Weights Regression - Recommendation
Page 196 and 197: Classification III - Music Genre Cl
Page 198 and 199: Chapter 9 Matplotlib provides the c
Page 200 and 201: [ 185 ] Chapter 9
Page 202 and 203: Chapter 9 def create_fft(fn): sampl
Page 204 and 205: Chapter 9 ax.set_yticks(range(len(g
Page 208 and 209: [ 193 ] Chapter 9 Improving classif
Page 210 and 211: We get the following promising resu
Page 212: Chapter 9 Summary In this chapter,
Page 215 and 216: Computer Vision - Pattern Recogniti
Page 237 and 238: Dimensionality Reduction Sketching
Page 239 and 240: Dimensionality Reduction However, t
Page 241 and 242: Dimensionality Reduction To underst
Page 243 and 244: Dimensionality Reduction In order t
Page 245 and 246: Dimensionality Reduction Asking the
Page 247 and 248: Dimensionality Reduction n_ feature
Page 249 and 250: Dimensionality Reduction Sketching
Page 251 and 252: Dimensionality Reduction Limitation
Page 253 and 254: Dimensionality Reduction Now, MDS t
Page 255 and 256: Dimensionality Reduction Of course,
Page 257 and 258:
Big(ger) Data • Your algorithms c
Page 259 and 260:
Big(ger) Data sleep(4) return 2*x @
Page 261 and 262:
Big(ger) Data Looking under the hoo
Page 263 and 264:
Big(ger) Data def write_result(ofna
Page 265 and 266:
Big(ger) Data Amazon Web Services i
Page 267 and 268:
Big(ger) Data In EC2 parlance, a ru
Page 269 and 270:
Big(ger) Data In this system, pip i
Page 271 and 272:
Big(ger) Data Keys, keys, and more
Page 273 and 274:
Big(ger) Data We can use the same j
Page 276 and 277:
Where to Learn More about Machine L
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
Page 289 and 290:
NumPy Beginner's Guide - Second Edi
show all

Building Machine Learning Systems with Python - Richert, Coelho

Create successful ePaper yourself

Delete template?

Save as template?