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Scored across 7 tools
The tools cover distinct stages of a tabular ML workflow, and descriptions clarify boundaries. Minor overlap exists between profile_dataset and inspect_competition (both inspect data) and among cv_baseline, explain_model, and fit_predict (all fit models), but each targets a different output.
All names are snake_case and mostly follow a verb_noun pattern (profile_dataset, compare_train_test, explain_model, check_submission). cv_baseline is a noun phrase and fit_predict contains two verbs, but the overall style remains consistent and readable.
Seven tools form a tight workflow from competition inspection to submission validation. Every tool has a clear role and none feels redundant or missing for the intended scope.
The surface covers inspection, profiling, train/test comparison, cross-validation, model explanation, prediction, and submission validation—the core lifecycle for tabular competitions. Minor gaps remain: no explicit tool for joining extra tables or handling multi-table relations, and no tuning or feature-engineering beyond a baseline.