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Cross-validation done right: k-fold, stratification, and time-series traps
A machine learning model can achieve 97% cross-validation accuracy and still fail spectacularly when deployed. The culprit is often not the algorithm, hyperparameters, or training data. It’s the way t
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Monitoring forecasts like a process engineer: control charts, vintage envelopes, and revision charts
A forecast can have an acceptable mean absolute error and still be operationally dangerous.
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Forecasting with XGBoost: turning a time series into a supervised problem without cheating
XGBoost does not know what a time series is.
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Rolling windows and lag features: time series into supervised learning without leaking the future
A time series becomes a supervised-learning table when each row represents a prediction decision: