PredictEvaluateCorrectEvaluate

PredictEvaluateCorrectEvaluate is a coined term describing a cyclical framework for developing and refining predictive systems through repeated anticipation, assessment, and adjustment. It emphasizes iterative learning where forecasts or decisions are continuously improved by measuring performance and applying targeted corrections.

In the Predict phase, a model or rule produces outputs, predictions, or decisions based on available inputs. This step encapsulates the core forecasting or control mechanism, producing results that will be scrutinized in the next stage. The Evaluate phase involves quantifying how well the predictions meet predefined criteria. Metrics may include accuracy, error, calibration, reliability, fairness, and uncertainty, often using holdout or cross-validated data to guard against overfitting. This phase also considers potential biases and limitations of the evaluation setup.

The Correct phase applies changes to improve future predictions. Corrections can take many forms, such as updating model parameters, redesigning features, incorporating new data, adjusting decision thresholds, or revising data pipelines and quality controls. The goal is to address identified gaps without introducing new biases or instability. The second Evaluate phase then reassesses the updated system to determine if the desired improvement was achieved and whether further iterations are warranted.

Applications of this framework span machine learning pipelines, forecasting, risk assessment, and decision-support systems, especially where ongoing accuracy and fairness are critical. Challenges include selecting appropriate metrics, avoiding leakage, managing computational cost, and preventing overfitting through excessive iteration.