Materials on AI, Python, Git, SQL, SAP/ERP, statistics and time series: explanations, examples, practical tips, sources and applicability limits.
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Choose a regression metric for non-negative data by first deciding the prediction goal, then picking a consistent loss such as MAE, RMSE, or pinball loss, and finally comparing against a constant baseline with a D2-style score.
A practical guide to selecting a decision threshold by weighting false‑positive and false‑negative costs, using a held‑out validation set and scikit‑learn confusion matrices.
Prediction intervals aim to contain future individual observations, while confidence intervals target a mean or other parameter, and the two are not interchangeable. Empirical coverage is the held-out fraction inside the interval and width is the distance between quantile limits; a constructed five-value example gives 80 percent coverage and mean width 1.8.
Build a previous-season reference, evaluate it on later observations and distinguish calendar alignment from leakage and missing data.
Use chronological splits and fit preprocessing only on past data.