Building Machine Learning Systems with Python - Richert, Coelho

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Dimensionality Reduction Garbage in, garbage out, that's what we know from real life. Throughout this book, we have seen that this pattern also holds true when applying machine learning methods to training data. Looking back, we realize that the most interesting machine learning challenges always involved some sort of feature engineering, where we tried to use our insight into the problem to carefully craft additional features that the machine learner hopefully picks up. In this chapter, we will go in the opposite direction with dimensionality reduction involving cutting away features that are irrelevant or redundant. Removing features might seem counter-intuitive at first thought, as more information is always better than less information. Shouldn't the unnecessary features be ignored after all? For example, by setting their weights to 0 inside the machine learning algorithm. The following are several good reasons that are still in practice for trimming down the dimensions as much as possible: • Superfluous features can irritate or mislead the learner. This is not the case with all machine learning methods (for example, Support Vector Machines love high-dimensional spaces). But most of the models feel safer with less dimensions. • Another argument against high-dimensional feature spaces is that more features mean more parameters to tune and a higher risk of overfitting. • The data we retrieved to solve our task might just have artificial high dimensions, whereas the real dimension might be small. • Less dimensions mean faster training and more variations to try out, resulting in better end results. • If we want to visualize the data, we are restricted to two or three dimensions. This is known as visualization. So, here we will show you how to get rid of the garbage within our data while keeping the valuable part of it.
Dimensionality Reduction Sketching our roadmap Dimensionality reduction can be roughly grouped into feature selection and feature extraction methods. We have already employed some kind of feature selection in almost every chapter when we invented, analyzed, and then probably dropped some features. In this chapter, we will present some ways that use statistical methods, namely correlation and mutual information, to be able to do feature selection in vast feature spaces. Feature extraction tries to transform the original feature space into a lower-dimensional feature space. This is useful especially when we cannot get rid of features using selection methods, but we still have too many features for our learner. We will demonstrate this using principal component analysis (PCA), linear discriminant analysis (LDA), and multidimensional scaling (MDS). Selecting features If we want to be nice to our machine learning algorithm, we will provide it with features that are not dependent on each other, yet highly dependent on the value to be predicted. It means that each feature adds some salient information. Removing any of the features will lead to a drop in performance. If we have only a handful of features, we could draw a matrix of scatter plots – one scatter plot for every feature-pair combination. Relationships between the features could then be easily spotted. For every feature pair showing an obvious dependence, we would then think whether we should remove one of them or better design a newer, cleaner feature out of both. Most of the time, however, we have more than a handful of features to choose from. Just think of the classification task where we had a bag-of-words to classify the quality of an answer, which would require a 1,000 by 1,000 scatter plot. In this case, we need a more automated way to detect the overlapping features and a way to resolve them. We will present two general ways to do so in the following subsections, namely filters and wrappers. [ 222 ]
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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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Regression - Recommendations Improv
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sklearn.naive_bayes package 127 skl
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NumPy Beginner's Guide - Second Edi
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