Practical explanations of mathematical models: overfitting, quality evaluation, result checks and limits on conclusions.
Compare a model's conclusions with the task conditions and evaluation method. Explanations and checks help you recognize overfitting and understand where results become unreliable.
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.
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.
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.
See why groups of different sizes give a combined mean of 18 rather than 15, with a short Python example.
Read training and validation scores together, fit preprocessing inside a pipeline and keep a final test set out of model selection.
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.