| { |
| "id": "ml12-root", |
| "requirements": "A credible experiment comparing clustering algorithms across synthetic geometric datasets: clustering conditions are implemented, execution covers multiple datasets with repeated seeds and ARI-centric reporting, and the analysis addresses H1/H2/H3 directionally.", |
| "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Well-motivated algorithm substitutes (e.g., HDBSCAN for DBSCAN) that test the same question should be credited.", |
| "weight": 1, |
| "sub_tasks": [ |
| { |
| "id": "ml12-code", |
| "requirements": "The clustering conditions and dataset generators are implemented correctly.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml12-code-algos", |
| "requirements": "The submission implements multiple distinct clustering conditions \u2014 typically k-means, agglomerative, DBSCAN, and/or spectral \u2014 via sklearn APIs with separate code paths and method-specific hyperparameters.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Method Implementation" |
| }, |
| { |
| "id": "ml12-code-data", |
| "requirements": "The submission generates multiple synthetic datasets with varied geometry (e.g., moons, circles, anisotropic blobs), preserves ground-truth labels, and standardizes features before clustering where appropriate.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Dataset and Model Acquisition" |
| }, |
| { |
| "id": "ml12-code-setup", |
| "requirements": "The experiment framework includes seed control and a consistent per-dataset protocol for cluster-count-dependent methods.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Experimental Setup" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml12-exec", |
| "requirements": "Execution produces clustering metrics for each condition.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml12-exec-metrics", |
| "requirements": "Execution outputs a metrics artifact containing numeric adjusted_rand_score (or equivalent) for each implemented condition on at least one dataset.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml12-exec-repeats", |
| "requirements": "Reported results are aggregated over multiple random seeds per (dataset, condition) with a dispersion estimate. 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": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml12-exec-tuning", |
| "requirements": "For DBSCAN (or equivalent density-based method), the run documents a reasonable choice of eps/min_samples (either via a small search or a justified fixed value), logged in the artifacts.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Hyperparameter Tuning" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml12-results", |
| "requirements": "Results address H1/H2/H3 directionally with interpretable evidence.", |
| "weight": 3, |
| "sub_tasks": [ |
| { |
| "id": "ml12-result-h1", |
| "requirements": "The submission compares ARI of density-based / spectral methods against k-means on nonlinear-geometry datasets and conveys whether they produce meaningfully better cluster assignments \u2014 judge directionally against H1.", |
| "weight": 25.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml12-result-h2h3", |
| "requirements": "The submission conveys dataset-wise ARI outcomes \u2014 whether agglomerative-ward is at least competitive with k-means on anisotropic blobs (H2) and whether any single method wins across all datasets (H3).", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml12-result-writeup", |
| "requirements": "The README or writeup describes setup, key ARI/NMI findings per dataset, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (synthetic-only scope, hyperparameter sensitivity, seed count, metric dependence). 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 |
| } |
|
|
