Practical guides to AI, Python, Git, SQL, Web/API, SAP/ERP and mathematical models, with explanations, code examples, sources and limitations.
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Learn how to effectively evaluate the precision and recall of AI models, especially when working with small question datasets. This guide covers the concepts, calculation methods, and practical considerations using scikit-learn.
Understand the differences between Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), how to choose the right metric for your prediction task, and the impact of large errors on each.
Read training and validation scores together, fit preprocessing inside a pipeline and keep a final test set out of model selection.
Find the expensive step and distinguish estimates from measurements.
Diagnose repeated JOIN rows by counting matching keys, checking constraints and choosing whether the result should contain orders, items or summaries.
Understand IS NULL, three-valued logic and the difference between COUNT(*) and COUNT(column).
Build a previous-season reference, evaluate it on later observations and distinguish calendar alignment from leakage and missing data.