| { |
| "id": "ml24-root", |
| "requirements": "A credible experiment studying online learners (SGD logistic, Passive-Aggressive, online Naive Bayes, FTRL-style logistic, or equivalents) under concept drift: streaming prequential setup is implemented, execution covers multiple datasets with multiple seeds, and results address H1/H2/H3 directionally.", |
| "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Well-motivated online-learner substitutes or drift-stream variants that test the same scientific question should be credited.", |
| "weight": 1, |
| "sub_tasks": [ |
| { |
| "id": "ml24-code", |
| "requirements": "Online-learning conditions and drifted streaming setup are implemented correctly.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml24-code-methods", |
| "requirements": "The submission implements multiple distinct online methods, each as a real incremental update path (e.g., partial_fit or equivalent), not batch retraining.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Method Implementation" |
| }, |
| { |
| "id": "ml24-code-streaming", |
| "requirements": "A prequential test-then-train loop is implemented: each sample (or mini-batch) is predicted before being used for model update, with ordered stream processing.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Experimental Setup" |
| }, |
| { |
| "id": "ml24-code-datasets", |
| "requirements": "The submission uses multiple streams including at least one drifted stream and one no-drift or matched-control stream, generated from sklearn datasets or numpy synthesis.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Dataset and Model Acquisition" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml24-exec", |
| "requirements": "Execution reports prequential metrics for each condition.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml24-exec-metrics", |
| "requirements": "Execution produces a metrics artifact containing numeric prequential accuracy (or equivalent) for each implemented method on at least one dataset.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml24-exec-recovery", |
| "requirements": "Execution includes a post-drift recovery measure (e.g., fixed-window post-drift accuracy) with a documented computation referencing known drift points.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml24-exec-seeds", |
| "requirements": "Each reported (dataset, method) metric is averaged over multiple random seeds, with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Hyperparameter Tuning" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml24-results", |
| "requirements": "Quantitative analysis addresses H1/H2/H3 directionally.", |
| "weight": 3, |
| "sub_tasks": [ |
| { |
| "id": "ml24-result-h1", |
| "requirements": "The submission compares prequential accuracy of margin-based online methods (Passive-Aggressive, FTRL) against online Naive Bayes across drifted datasets and conveys whether margin-based methods are meaningfully better \u2014 judge directionally against H1.", |
| "weight": 20.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml24-result-h2", |
| "requirements": "The submission reports post-drift recovery comparisons and conveys whether Passive-Aggressive or FTRL tends to lead on most datasets (H2).", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml24-result-h3", |
| "requirements": "The submission compares drifted vs no-drift control performance for the implemented methods and conveys whether drift produces a meaningful accuracy degradation (H3).", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml24-result-writeup", |
| "requirements": "The README or writeup describes setup, key metrics, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (stream realism, drift design choices, seed count, metric assumptions). 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 |
| } |
|
|
