| { |
| "id": "ml10-root", |
| "requirements": "A credible experiment studying how cross-validation strategy choice (k-fold, stratified k-fold, repeated stratified k-fold, LOOCV) affects small-sample model-selection reliability: strategies are implemented consistently, runs cover multiple small-sample datasets with multiple seeds, and results address H1/H2/H3 directionally around estimation bias, stability, and compute.", |
| "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Alternative CV strategies or datasets that preserve the scientific question (small-sample bias/stability of CV) should be credited.", |
| "weight": 1, |
| "sub_tasks": [ |
| { |
| "id": "ml10-code", |
| "requirements": "Cross-validation strategy conditions and the model-selection pipeline are implemented correctly.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml10-code-cv-strategies", |
| "requirements": "The submission implements multiple distinct CV strategies \u2014 typically including plain k-fold, stratified k-fold, repeated stratified k-fold, and optionally LOOCV \u2014 as separate code paths used for model selection under a shared candidate-model grid.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Method Implementation" |
| }, |
| { |
| "id": "ml10-code-datasets", |
| "requirements": "The submission uses multiple small-sample datasets (e.g., reduced breast_cancer, wine, or synthetic imbalanced variants) with a held-out test split.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Dataset and Model Acquisition" |
| }, |
| { |
| "id": "ml10-code-bias-metric", |
| "requirements": "Code computes an estimation-bias style metric (e.g., |best_cv_score - selected_model_test_score|) or equivalent for each (dataset, seed, strategy) and aggregates it reproducibly.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml10-exec", |
| "requirements": "Execution logs required metrics per strategy with multi-seed evaluation.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml10-exec-metrics-output", |
| "requirements": "Execution produces a machine-readable results artifact containing estimation bias (or equivalent) and selected-model test accuracy for each implemented strategy on at least one dataset.", |
| "weight": 16.6667, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml10-exec-seeds-runtime", |
| "requirements": "Execution uses multiple random seeds per (dataset, strategy) and records timing information sufficient to compare the more expensive strategies (e.g., LOOCV vs repeated stratified). Honest small-seed runs with variance reported are preferable to a single run.", |
| "weight": 8.3333, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Experimental Setup" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml10-results", |
| "requirements": "Reported results address H1/H2/H3 directionally with interpretable evidence.", |
| "weight": 3, |
| "sub_tasks": [ |
| { |
| "id": "ml10-result-h1", |
| "requirements": "The submission compares mean estimation bias of repeated-stratified CV vs plain k-fold per dataset and conveys whether repeated stratified is meaningfully better on most datasets \u2014 judge directionally against H1.", |
| "weight": 25.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml10-result-h2h3", |
| "requirements": "The submission reports selection stability or across-seed variability for stratified k-fold vs plain k-fold (H2) and compares LOOCV versus repeated stratified CV on estimation bias and runtime (H3), conveying qualitative outcomes for both.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml10-result-writeup", |
| "requirements": "The README or writeup describes the setup, reports per-strategy metrics, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and discusses limitations (small dataset scope, candidate-model grid choice, metric sensitivity, compute budget). No strict word-count requirement.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| } |
|
|
