Building Machine Learning Systems with Python - Richert, Coelho

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coefficients = [] # We are now going to run a leave-1-out cross-validation loop for u in xrange(reviews.shape[0]): # for all user ids es0 = np.delete(es,u,1) # all but user u r0 = np.delete(reviews, u, 0) P0,P1 = np.where(r0 > 0) # we only care about actual predictions X = es[:,P0,P1] y = r0[r0 > 0] reg.fit(X.T,y) coefficients.append(reg.coef_) prediction = reg.predict(es[:,u,reviews[u] > 0].T) # measure error as before The result is an RMSE of almost exactly 1. We can also analyze the coefficients variable to find out how well our predictors fare: print coefficients.mean(0) # the mean value across all users Chapter 8 The values of the array are [ 0.25164062, 0.01258986, 0.60827019]. The estimate of the most similar movie has the highest weight (it was the best individual prediction, so it is not surprising), and we can drop the correlation-based method from the learning process as it has little influence on the final result. What this setting does is it makes it easy to add a few extra ideas; for example, if the single most similar movie is a good predictor, how about we use the five most similar movies in the learning process as well? We can adapt the earlier code to generate the k-th most similar movie and then use the stacked learner to learn the weights: es = [ usermodel.estimate_all() similar_movie.estimate_all(k=1), similar_movie.estimate_all(k=2), similar_movie.estimate_all(k=3), similar_movie.estimate_all(k=4), similar_movie.estimate_all(k=5), ] # the rest of the code remains as before! We gained a lot of freedom in generating new machine learning systems. In this case, the final result is not better, but it was easy to test this new idea. [ 171 ]
Regression – Recommendations Improved However, we do have to be careful to not overfit our dataset. In fact, if we randomly try too many things, some of them will work well on this dataset but will not generalize. Even though we are using cross-validation, we are not cross-validating our design decisions. In order to have a good estimate, and if data is plentiful, you should leave a portion of the data untouched until you have your final model that is about to go into production. Then, testing your model gives you an unbiased prediction of how well you should expect it to work in the real world. Basket analysis The methods we have discussed so far work well when you have numeric ratings of how much a user liked a product. This type of information is not always available. Basket analysis is an alternative mode of learning recommendations. In this mode, our data consists only of what items were bought together; it does not contain any information on whether individual items were enjoyed or not. It is often easier to get this data rather than ratings data as many users will not provide ratings, while the basket data is generated as a side effect of shopping. The following screenshot shows you a snippet of Amazon.com's web page for the book War and Peace, Leo Tolstoy, which is a classic way to use these results: This mode of learning is not only applicable to actual shopping baskets, naturally. It is applicable in any setting where you have groups of objects together and need to recommend another. For example, recommending additional recipients to a user writing an e-mail is done by Gmail and could be implemented using similar techniques (we do not know what Gmail uses internally; perhaps they combine multiple techniques as we did earlier). Or, we could use these methods to develop an application to recommend webpages to visit based on your browsing history. Even if we are handling purchases, it may make sense to group all purchases by a customer into a single basket independently of whether the items where bought together or on separate transactions (this depends on the business context). [ 172 ]
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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
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,
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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
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Page 206 and 207: Chapter 9 On the left-hand side gra
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,
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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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