Building Machine Learning Systems with Python - Richert, Coelho

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Chapter 11 Let us have a look at a slightly more complex Iris dataset. We will use it later to contrast LDA with PCA. The Iris dataset contains four attributes per flower. With the previous code, we would project it into a three-dimensional space while keeping the relative distances between the individual flowers as much as possible. In the previous example, we did not specify any metric, so MDS will default to Euclidean. This means that flowers that were different according to their four attributes should also be far away in the MDS-scaled three-dimensional space, and flowers that were similar should be near together now, as shown in the following screenshot: Doing the dimensional reduction to three and two dimensions with PCA instead, we see the expected bigger spread of the flowers belonging to the same class, as shown in the following screenshot: [ 239 ]
Dimensionality Reduction Of course, using MDS requires an understanding of the individual feature's units; maybe we are using features that cannot be compared using the Euclidean metric. For instance, a categorical variable, even when encoded as an integer (0 = red circle, 1 = blue star, 2 = green triangle, and so on), cannot be compared using Euclidean (is red closer to blue than to green?). But once we are aware of this issue, MDS is a useful tool that reveals similarities in our data that otherwise would be difficult to see in the original feature space. Looking a bit deeper into MDS, we realize that it is not a single algorithm, but a family of different algorithms, of which we have used just one. The same was true for PCA, and in case you realize that neither PCA nor MDS solves your problem, just look at the other manifold learning algorithms that are available in the Scikit-learn toolkit. Summary We learned that sometimes we can get rid of all the features using feature selection methods. We also saw that in some cases this is not enough, and we have to employ feature extraction methods that reveal the real and the lower-dimensional structure in our data, hoping that the model has an easier game with it. We have only scratched the surface of the huge body of available dimensionality reduction methods. Still, we hope that we have got you interested in this whole field, as there are lots of other methods waiting for you to pick up. At the end, feature selection and extraction is an art, just like choosing the right learning method or training model. The next chapter covers the use of Jug, a little Python framework to manage computations in a way that takes advantage of multiple cores or multiple machines. We will also learn about AWS – the Amazon cloud. [ 240 ]
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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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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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Building Machine Learning Systems with Python - Richert, Coelho

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