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Artificial Intelligence Task

Clustering

Artificial Intelligence models are able to learn to sample a subset of data points from a larger dataset (usuarlly very large ones) to match one or many goals or criterias. The goal of sampling is to ensure that the selected data points are somehow meaningfully connected to the overall dataset, while also reducing the computational burden and time required to process large amounts of data.

Input

Unlabeled data ocurrences of many types

Output

Grouping of data points into pre-provided or spontaneous groups based on similarity

Goal

To correctly assign each ocurrence to an inherent or provided cluster

Learning Strategy

Classification algorithms adapted for multi-label output

Evaluation Metric

Hamming loss, precision, recall, and F1 score for multi-label settings.

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