February 2019
Beginner to intermediate
382 pages
10h 1m
English
Content preview from Python Machine Learning By Example - Second Edition
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Exercises
- Do you think all of the top 500 word tokens contain valuable information? If not, can you impose another list of stop words?
- Can you use stemming instead of lemmatization to process the newsgroups data?
- Can you increase max_features in CountVectorizer from 500 to 5000 and see how the t-SNE visualization will be affected?
- Try visualizing documents from six topics (similar or dissimilar) and tweak parameters so that the formed clusters look reasonable.