bettersampled

Bettersampled is a term used to describe a family of data sampling strategies and related software approaches designed to produce higher-quality samples from a dataset than simple random sampling. The objective is to improve representativeness, reduce labeling and measurement costs, and support more reliable statistical estimates and machine learning model performance.

Methodology generally combines stratified sampling, importance sampling, and adaptive or progressive resampling guided by feedback. Signals used to prioritize samples include data density, feature coverage, label distribution, and model uncertainty. In practice, bettersampled methods may employ active learning loops, recalibrating sampling quotas as new information about the population or the model behavior becomes available.

Applications include curating training datasets for machine learning, designing efficient surveys, downsampling time-series while preserving rare events, and improving anomaly detection datasets. The approach can help maintain coverage of underrepresented regions of the feature space while focusing labeling effort on informative or uncertain instances.

Advantages include improved representativeness, better generalization in some settings, and reduced labeling costs. Potential drawbacks include computational overhead, methodological complexity, and susceptibility to bias if feedback signals reflect preexisting model biases or incorrect priors. Ongoing validation is typically required to ensure that sampling objectives remain aligned with analysis goals.

Related concepts include active learning, stratified sampling, importance sampling, downsampling, and dataset curation.