| { |
| "id": "ml02-root", |
| "requirements": "A credible experiment studying bagging vs boosting vs stacking for noisy non-linear regression: the main ensemble families are implemented, execution covers multiple regression datasets with reasonable seed coverage, and the analysis addresses H1/H2/H3 using RMSE-centered evidence.", |
| "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact threshold satisfaction. Condition-name variants (e.g., 'RandomForest' vs 'bagging_random_forest') should not be penalized when the underlying method is clearly the intended one.", |
| "weight": 1, |
| "sub_tasks": [ |
| { |
| "id": "ml02-code", |
| "requirements": "The regression ensemble conditions and datasets are implemented in a way that supports a fair comparison.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml02-code-conditions", |
| "requirements": "The submission implements the main ensemble families relevant to the hypotheses \u2014 a bagging-style model (e.g., RandomForestRegressor), a boosting-style model (e.g., GradientBoostingRegressor), and a stacking-style model (e.g., StackingRegressor) \u2014 alongside a non-ensemble baseline such as a single DecisionTreeRegressor. Equivalent well-motivated choices are acceptable.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Method Implementation" |
| }, |
| { |
| "id": "ml02-code-datasets", |
| "requirements": "The submission uses multiple regression datasets that allow a noise-robustness comparison \u2014 for example, friedman1 at varying noise levels and/or diabetes from sklearn \u2014 with a reasonable train/test split.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Dataset and Model Acquisition" |
| }, |
| { |
| "id": "ml02-code-setup", |
| "requirements": "The pipeline uses a consistent preprocessing and evaluation setup across conditions, with controlled random-state handling and no obvious test-set leakage into training or stacking meta-features.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Experimental Setup" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml02-exec", |
| "requirements": "Execution logs the core metrics needed to evaluate the hypotheses.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml02-exec-metrics", |
| "requirements": "Execution produces a machine-readable metrics artifact containing numeric RMSE for the implemented (dataset, condition) cells, plus at least one complementary error metric (MAE or R\u00b2). Complete coverage is preferred; partial coverage should be scored proportional to how much of the hypothesis-relevant grid is populated.", |
| "weight": 16.6667, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml02-exec-seeds", |
| "requirements": "Reported dataset-condition results are aggregated over multiple random seeds with some form of dispersion reporting (std, stderr, CI, or min/max). More seeds are better, but an honest small-seed run with reported variance is preferable to a single deterministic run.", |
| "weight": 8.3333, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml02-results", |
| "requirements": "Results analysis addresses all three hypotheses with quantitative evidence.", |
| "weight": 3, |
| "sub_tasks": [ |
| { |
| "id": "ml02-result-h1", |
| "requirements": "The submission compares bagging (e.g., Random Forest) vs boosting (e.g., GBRT) on RMSE in a high-noise regression regime and conveys whether bagging is clearly better, clearly worse, or comparable \u2014 judge directionally against H1.", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml02-result-h2", |
| "requirements": "The submission conveys whether ensembles deliver a meaningful RMSE improvement over the single-tree baseline on most of the evaluated datasets. Exact percentage thresholds are not required; a clear comparison grounded in the reported numbers suffices.", |
| "weight": 20.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml02-result-h3", |
| "requirements": "The submission ranks ensemble and baseline conditions by RMSE across the evaluated datasets and discusses whether stacking is competitive (best or near-best on some datasets, not systematically worst) \u2014 judge directionally against H3.", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml02-result-writeup", |
| "requirements": "The README or writeup describes the methods and setup, presents the key RMSE/MAE/R\u00b2 findings, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations. No strict word-count requirement.", |
| "weight": 10.0, |
| "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 |
| } |
|
|
