Lecture
Data Collection and Preparation
Description
This lecture covers the importance of data collection and preparation in the context of classification methodology, emphasizing the steps involved such as feature identification, labeling, discretization, selection, and normalization. The instructor discusses the challenges of labeling data, the different types of features, and the methods to obtain labels, including crowdsourcing. Various aggregation algorithms for handling crowd-worker answers are also explained.
Official source
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