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.
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.
A practical guide to choosing precision-oriented metrics, threshold analysis, and confusion matrix decomposition in scikit-learn for minimizing false positive rates in binary classification tasks.
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.
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.
Read training and validation scores together, fit preprocessing inside a pipeline and keep a final test set out of model selection.