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.
Step-by-step guide to creating an index, generating embeddings, and executing hybrid queries that combine full-text and vector search using Azure AI Search.
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.
How retrieval connects a language model to documents, and why a citation is not a guarantee.
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.