Building Machine Learning Systems with Python - Richert, Coelho

Table of Contents
Building more complex classifiers 40
A more complex dataset and a more complex classifier 41
Learning about the Seeds dataset 42
Features and feature engineering 43
Nearest neighbor classification 44
Binary and multiclass classification 47
Summary 48
Chapter 3: Clustering – Finding Related Posts 49
Measuring the relatedness of posts 50
How not to do it 50
How to do it 51
Preprocessing – similarity measured as similar number
of common words 51
Converting raw text into a bag-of-words 52
Counting words 53
Normalizing the word count vectors 56
Removing less important words 56
Stemming 57
Installing and using NLTK 58
Extending the vectorizer with NLTK's stemmer 59
Stop words on steroids 60
Our achievements and goals 61
Clustering 62
KMeans 63
Getting test data to evaluate our ideas on 65
Clustering posts 67
Solving our initial challenge 68
Another look at noise 71
Tweaking the parameters 72
Summary 73
Chapter 4: Topic Modeling 75
Latent Dirichlet allocation (LDA) 75
Building a topic model 76
Comparing similarity in topic space 80
Modeling the whole of Wikipedia 83
Choosing the number of topics 86
Summary 87
Chapter 5: Classification – Detecting Poor Answers 89
Sketching our roadmap 90
Learning to classify classy answers 90
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