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.
prediction interval / confidence interval / quantile regression / empirical coverage / interval width / scikit-learn / calibration / held-out evaluation
See why groups of different sizes give a combined mean of 18 rather than 15, with a short Python example.
math
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.
regression / model evaluation / MAE / RMSE / error metrics / scikit-learn / machine learning
Read training and validation scores together, fit preprocessing inside a pipeline and keep a final test set out of model selection.
Machine Learning / Validation / Overfitting