Editing Human Centered Data Science (Fall 2019)/Assignments
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You are also expected to write a short reflection on the project, that focuses on how both your findings from this analysis and the process you went through to reach those findings helps you understand the causes and consequences of biased data in large, complex data science projects. | You are also expected to write a short reflection on the project, that focuses on how both your findings from this analysis and the process you went through to reach those findings helps you understand the causes and consequences of biased data in large, complex data science projects. | ||
''A repository with a README framework and examples of querying the ORES datastore in R and Python can be found [https://github.com/Ironholds/data-512-a2 here]'' | |||
==== Getting the article and population data ==== | ==== Getting the article and population data ==== | ||
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==== Getting article quality predictions ==== | ==== Getting article quality predictions ==== | ||
Now you need to get the predicted quality scores for each article in the Wikipedia dataset. We're using a machine learning system called [https://www.mediawiki.org/wiki/ORES ORES] ("Objective Revision Evaluation Service"). ORES estimates the quality of an article (at a particular point in time), and assigns a series of probabilities that the article is in one of 6 quality categories. The options are, from best to worst: | Now you need to get the predicted quality scores for each article in the Wikipedia dataset. We're using a machine learning system called [https://www.mediawiki.org/wiki/ORES ORES] ("Objective Revision Evaluation Service"). ORES estimates the quality of an article (at a particular point in time), and assigns a series of probabilities that the article is in one of 6 quality categories. The options are, from best to worst: |