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Data classification and labeling are key processes used to organize, process, and convert large datasets into actionable information. These concepts are extensively employed in data analytics, machine learning, and artificial intelligence.
Data classification involves categorizing data sets into specific groups or classes. This method determines the appropriate category for each data point and is commonly utilized within supervised learning frameworks, employing various algorithms.
Examples of algorithms used:
Applications:
Data labeling refers to assigning descriptive labels or annotations to data sets based on their content. This step is essential for creating training data, particularly in supervised learning environments. Labels assist algorithms in understanding and processing data effectively during the learning phase.
Labeling methods:
Applications:
