Classification

Artificial Intelligence 04 Sep 2026 9 min read

Handle Class Imbalance in Machine Learning

A classifier can achieve impressive accuracy while failing on the cases you care about most. If only 1% of transactions are fraudulent, a model that predicts “not fraud” for every transaction is 99% accurate and still useless for detecting fraud. This is the practical problem of class imbalance: some target classes appear much less often than others. Imbalance does not automatically make a dataset bad, and it does not imply that every model needs special treatment. It does mean that accuracy can hide important errors and that the training objective may give rare examples too little influence.

Artificial Intelligence Machine Learning

Artificial Intelligence 03 Sep 2026 9 min read

Label Smoothing in Classification Models

A classification model is often trained as if the correct class deserves all of the target probability and every other class deserves none. For a three-class problem, an example labeled cat might therefore use this target: cat: 1.00 dog: 0.00 fox: 0.00 That target is convenient, but it asks the model to push probability toward an extreme even when labels are imperfect, classes overlap, or the input is genuinely ambiguous. Label smoothing changes the training target so that a small amount of probability mass is assigned away from the labeled class.

Artificial Intelligence Machine Learning

Artificial Intelligence 03 Sep 2026 8 min read

Choose Classification Thresholds with Precision and Recall

A binary classifier often produces a score rather than a final yes-or-no answer. An image model might estimate a 0.82 probability that a component is defective, while a moderation model might assign a 0.37 score to unwanted content. The classification threshold turns that continuous score into a decision. A threshold of 0.5 is common, but it is not automatically correct. The right threshold depends on which mistakes matter, how frequently the positive class occurs, and what happens after the model makes a prediction.

Artificial Intelligence Machine Learning

Data Science 01 Sep 2026 5 min read

Probability Calibration for Classification Models

A classifier can rank examples correctly while producing probabilities that are poor estimates of real-world likelihood. If a model assigns 0.8 probability to many comparable cases, calibration asks whether roughly 80% of those cases are actually positive. This matters whenever probabilities drive decisions such as pricing, triage, alert thresholds, expected value, or human review. Discrimination and calibration are different Metrics such as ROC AUC evaluate how well a model ranks positive examples above negative ones. They do not require predicted probabilities to match observed frequencies.

Data Science Machine Learning

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