July 2017
Intermediate to advanced
360 pages
8h 26m
English
Content preview from Machine Learning Algorithms
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A sample text classifier based on the Reuters corpus
We are going to build a sample text classifier based on the NLTK Reuters corpus. This one is made of up thousands of news lines divided into 90 categories:
from nltk.corpus import reuters>>> print(reuters.categories())[u'acq', u'alum', u'barley', u'bop', u'carcass', u'castor-oil', u'cocoa', u'coconut', u'coconut-oil', u'coffee', u'copper', u'copra-cake', u'corn', u'cotton', u'cotton-oil', u'cpi', u'cpu', u'crude', u'dfl', u'dlr', u'dmk', u'earn', u'fuel', u'gas', u'gnp', u'gold', u'grain', u'groundnut', u'groundnut-oil', u'heat', u'hog', u'housing', u'income', u'instal-debt', u'interest', u'ipi', u'iron-steel', u'jet', u'jobs', u'l-cattle', u'lead', u'lei', u'lin-oil', u'livestock', u'lumber', ...
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