How can I know if my dataset is representative enough to build a model with vast information without bias?
Tuesday, 20 January 2026
by Adrià Comes Sanchis
The representativeness of a dataset is foundational to the development of reliable and unbiased machine learning models. Representativeness refers to the extent to which the dataset accurately reflects the real-world population or phenomenon that the model aims to learn about and make predictions on. If a dataset lacks representativeness, models trained on it are likely
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning
Tagged under:
Artificial Intelligence, Data Bias, Data Science, Dataset Quality, Ethics, Fairness, Google Cloud, Machine Learning, Model Evaluation

