I have a question that I would like to ask, for a data-driven task (for example, based on machine learning, etc.), what kind of data set is the advantage data set? Is there a qualitative or quantitative way to describe the quality of the data set?
Generally working with Real time datasets is appreciable.
if not available we can access data through Kaggle or Github or other sources like web logs, data marts, customer reviews ...
Quantitatively, the quality of a dataset can be assessed using various metrics, such as accuracy rates, error rates, missing data percentages, or statistical measures of distributional properties. Additionally, qualitative evaluations may involve domain experts reviewing the dataset for its relevance, completeness, and representativeness.
It's important to note that the specific requirements for an advantageous dataset can vary depending on the task at hand and the specific domain. Therefore, it's recommended to carefully analyze and curate the dataset to ensure its quality aligns with the objectives of your data-driven task.
The "advantageous" dataset for a data-driven task is one that is relevant, sufficiently large, high-quality, representative, balanced, temporally consistent, labeled, and ethically collected, supporting reliable model training and accurate predictions.
- Badges
- Science topic
- Similar topics
- Social Psychology
- Attitude