Machine Learning

98 articles

Data Science 01 Sep 2026 3 min read

Calibrate Classification Probabilities Before Using Decision Thresholds

A classifier can rank examples well while producing poor probability estimates. If predictions drive cost-sensitive decisions, triage, or risk thresholds, the difference matters. A score of 0.8 is useful as a probability only when similarly scored examples are positive about 80% of the time under the deployment distribution. Separate discrimination from calibration Metrics such as ROC AUC primarily measure ranking. Calibration asks whether predicted probabilities agree with observed frequencies. A model can have strong AUC and still be overconfident or underconfident.

Data Science Machine Learning

Data Science 01 Sep 2026 5 min read

Avoiding Data Leakage in Machine Learning Pipelines

Data leakage happens when information that would not be available at prediction time influences model training. The result is an evaluation score that looks excellent in development and collapses after deployment. Leakage is often subtle because the model code itself can be correct. The mistake lives in how datasets, features, preprocessing, and time boundaries are constructed. Split before learning from the data A classic mistake is standardizing the full dataset and splitting afterward.

Data Science Machine Learning