| { |
| "id": "ml11-root", |
| "requirements": "A credible experiment benchmarking unsupervised outlier detectors (IsolationForest, LocalOutlierFactor, OneClassSVM, EllipticEnvelope, or equivalents) with injected anomalies: methods are implemented as distinct code paths, runs cover multiple datasets across anomaly rates, 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 substitutes (e.g., HBOS, KNN-based outlier) that test the same scientific question should be credited.", |
| "weight": 1, |
| "sub_tasks": [ |
| { |
| "id": "ml11-code", |
| "requirements": "The outlier-detection conditions and anomaly-injection pipeline are implemented correctly.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml11-code-methods", |
| "requirements": "The submission implements multiple distinct detector code paths \u2014 typically including IsolationForest, LOF, OneClassSVM, and an elliptic/Gaussian-envelope method \u2014 rather than aliases to one shared estimator.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Method Implementation" |
| }, |
| { |
| "id": "ml11-code-injection", |
| "requirements": "A reproducible anomaly-injection routine is implemented for test data at one or more explicit contamination rates with binary ground-truth anomaly labels.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Experimental Setup" |
| }, |
| { |
| "id": "ml11-code-data", |
| "requirements": "The code uses multiple datasets (sklearn built-ins or comparable) and applies a consistent preprocessing pipeline (including feature scaling) before model fitting.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Dataset and Model Acquisition" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml11-exec", |
| "requirements": "Execution records anomaly-detection metrics for each condition.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml11-exec-metrics", |
| "requirements": "Execution produces a machine-readable metrics artifact containing numeric ROC-AUC and PR-AUC (or equivalents) for each implemented method on at least one (dataset, rate) cell.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml11-exec-seeds-rates", |
| "requirements": "Reported results aggregate over multiple random seeds and include at least two anomaly rates, 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": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml11-exec-runtime", |
| "requirements": "The run logs a wall-clock timing measure per method and demonstrates completion within a CPU-only workflow.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml11-results", |
| "requirements": "Quantitative analysis addresses H1/H2/H3 directionally.", |
| "weight": 3, |
| "sub_tasks": [ |
| { |
| "id": "ml11-result-h1", |
| "requirements": "The submission compares mean ROC-AUC of IsolationForest vs OneClassSVM per dataset and conveys whether IsolationForest 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": "ml11-result-h2h3", |
| "requirements": "The submission conveys whether LOF is competitive on PR-AUC for at least one dataset at a higher anomaly rate (H2) and whether detectors meaningfully outperform a random baseline on pooled ROC-AUC (H3).", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml11-result-writeup", |
| "requirements": "The README or writeup describes setup and anomaly injection, reports ROC-AUC/PR-AUC results, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and discusses limitations (synthetic anomalies, dataset scope, hyperparameter sensitivity, seed count). 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 |
| } |
|
|
