v0.4.1

This release reorders the run report so it reads as the pipeline ran, fixes a crash in the XGBoost candidate on string class labels, and adds an on-demand production test suite that was run in full against real datasets, the real boosters, and 100000-row data.

Changed

• The report now reads in the order the pipeline actually runs: run summary, data profile, data quality, preprocessing, split, model shortlist, tuning and selection, evaluation, limitations and next steps, and the full decision log last. The data profile gains a per-column table (type, missingness, cardinality, and distribution statistics for numeric columns) shown up front instead of after the model results. No content was removed. In the HTML report each chart stays with its section: the target distribution and feature correlation sit with the profile, and the confusion matrix, ROC, precision-recall, residual, and predicted-versus-actual charts sit in evaluation. Tests assert the section order for a classification run and a regression run.

Fixed

• The XGBoost classification candidate crashed any run whose target held string labels (for example good/bad or <=50K/>50K), because XGBoost only accepts consecutive integer classes. The candidate is now wrapped in an adapter that encodes the labels during fit and returns the original labels from predict, with probability columns following sorted label order. LightGBM and CatBoost already handled string labels. The offline suite covers all three boosters when they are installed.

Added

• An on-demand production test suite under production_tests/, separate from the offline unit suite: real public datasets (OpenML and scikit-learn) end to end with save, load, and predict round-trips, real booster training and selection, and scale runs at 1000, 10000, and 100000 rows with recorded run times and memory readings. It needs the network and is not part of normal CI. The full pass ran green: all eight datasets, all three boosters, and all three scale sizes.