Data Science Methods for Uncertain Data
Data Science Methods for Uncertain Data Uncertainty is a fact of any data project. Data can be noisy, incomplete, biased, or collected under changing conditions. By recognizing this, data scientists can design analyses that reveal not just a single answer, but the likely range around it. This helps teams make wiser choices and avoid overconfident conclusions. Understanding uncertainty in data Uncertainty comes from several sources: missing values, measurement error, sampling bias, and model assumptions. It shows up in predictions as intervals, not fixed numbers. A clear view of this uncertainty makes results more trustworthy and usable in real decisions. ...