Free AI & machine learning lessons
Targets, data, baselines, and leakage
Define who uses the output, which action it changes, when features are available, and how success is measured. The label is a proxy for the real outcome and can encode historical bias or process quirks. A simple heuristic or linear baseline reveals whether complexity earns its cost. Leakage occurs when training sees information unavailable at decision time.
For rare-event prediction, always guessing “no” can have high accuracy while providing no value. Compare against prevalence, a business rule, or a simple model using the metric and cost that matter. If a complex model barely improves decisions, its latency, maintenance, and failure surface may not be justified.
Models, loss, gradients, and regularization
Examples flow forward to predictions. A loss turns prediction error into an optimization signal. Gradients estimate how parameter changes affect that loss; an optimizer updates parameters across batches. Regularization, architecture, and data augmentation constrain the search toward functions likely to generalize rather than memorize.
High training and validation error suggest the model, features, or optimization cannot capture enough signal. Low training error with worsening validation error suggests memorization or distribution mismatch. More capacity helps the first case and can worsen the second; regularization or better data may help the second.
Generalization, metrics, calibration, and uncertainty
Random splits can leak people, time, devices, or near-duplicates across train and test. Temporal or group-based splits better simulate many deployments. Precision and recall describe different error tradeoffs; calibration asks whether predicted probabilities match frequencies; confidence intervals reflect sampling uncertainty. Slice analysis finds failures hidden by an average.
A model can rank risky cases well but output probabilities that are systematically too high. Ranking metrics may look good while a “0.8” score corresponds to only 0.5 observed frequency. If decisions multiply probability by cost, calibration matters directly and should be checked over time and subgroups.
Deployment, drift, feedback, safety, and human judgment
The model influences decisions, which influence future labels and who appears in the data. Feature pipelines can skew between training and serving. Populations, incentives, and measurement systems drift. Production therefore needs input validation, shadow or staged rollout, outcome monitoring, fallback behaviour, auditability, and retraining criteria.
Track schema and feature health, input distribution, prediction distribution, latency, decision rate, delayed outcomes, calibration, subgroup performance, and business guardrails. A shifted input is a warning, not proof of harm; unchanged inputs do not prove outcomes are sound. Link alerts to investigation and rollback actions.