| { |
| "id": "ml04-root", |
| "requirements": "A credible experiment studying how feature scaling (standard, min-max, robust, or none) affects KNN classification on datasets with different distributions: scaling conditions are implemented consistently, execution covers multiple datasets and random splits, and conclusions address H1/H2/H3 directionally.", |
| "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. If the submission uses alternative but well-motivated scalers or datasets that test the same scientific question, credit it accordingly.", |
| "weight": 1, |
| "sub_tasks": [ |
| { |
| "id": "ml04-code", |
| "requirements": "The scaling conditions and KNN setup are implemented in a way that supports a fair comparison.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml04-code-conditions", |
| "requirements": "The submission implements the relevant scaling conditions \u2014 typically no-scaling, standard-scaler, min-max-scaler, and robust-scaler \u2014 as distinct code paths sharing the same KNN configuration.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Method Implementation" |
| }, |
| { |
| "id": "ml04-code-datasets", |
| "requirements": "The submission uses multiple datasets including at least one real sklearn dataset and at least one synthetic/outlier-heavy dataset, with a reasonable train/test split.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Dataset and Model Acquisition" |
| }, |
| { |
| "id": "ml04-code-consistency", |
| "requirements": "The implementation keeps KNN settings identical across scaling conditions and fits preprocessing only on training data within each split.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Experimental Setup" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml04-exec", |
| "requirements": "Execution produces benchmark metrics adequate to evaluate the hypotheses.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml04-exec-metrics", |
| "requirements": "Execution produces a machine-readable metrics artifact with numeric test accuracy and an additional metric such as macro-F1 for each evaluated (condition, dataset) cell.", |
| "weight": 16.6667, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml04-exec-splits", |
| "requirements": "Reported metrics are aggregated over multiple random splits or seeds per (condition, dataset) with some dispersion measure (std or CI). More splits are better, but an honest small-split run with variance reported is preferable to a single split.", |
| "weight": 8.3333, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml04-results", |
| "requirements": "The analysis evaluates all three hypotheses and presents interpretable findings.", |
| "weight": 3, |
| "sub_tasks": [ |
| { |
| "id": "ml04-result-h1", |
| "requirements": "The submission compares robust-scaling vs standard-scaling + KNN on the outlier-heavy dataset and conveys whether robust-scaling shows a meaningful accuracy advantage. Exact percentage-point thresholds are not required.", |
| "weight": 20.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml04-result-h2", |
| "requirements": "The submission discusses whether scaler choice materially affects KNN accuracy on at least one real dataset \u2014 i.e., whether the best-vs-worst-scaler gap is non-trivial and of practical significance.", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml04-result-h3", |
| "requirements": "The submission compares the no-scaling baseline against the scaled conditions and conveys whether no-scaling tends to be clearly suboptimal across the evaluated datasets.", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml04-result-writeup", |
| "requirements": "The README or writeup describes setup and datasets, reports the key metric values per condition, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations such as dataset scope, split count, or KNN-hyperparameter sensitivity. 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 |
| } |
|
|
