Best Data labeling summary (updating)


Data labeling summary (1) 1. Under supervised learning, a large amount of (labeled) data is required. 2. Reasons for data noise: Problems with data collection tools Data entry, transmission errors technical limitations 3. On the basis of the import, complete (data cleaning) and preprocessing work for missing information, inconsistent information and redundant information. 4. In … Read more