| { |
| "id": "ml15-root", |
| "requirements": "A credible experiment studying filter-based feature selection (mutual information, chi-square, ANOVA F) versus embedded L1-logistic selection under injected irrelevant features: conditions are implemented, runs cover multiple datasets with multiple seeds and injection levels, and results address H1/H2/H3 directionally.", |
| "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Alternative filter methods or embedded selectors (e.g., tree-based importance) that test the same scientific question should be credited.", |
| "weight": 1, |
| "sub_tasks": [ |
| { |
| "id": "ml15-code", |
| "requirements": "Feature-selection conditions and the noise-injection pipeline are implemented correctly.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml15-code-conditions", |
| "requirements": "The submission implements multiple distinct selection conditions \u2014 typically including mutual information, chi-square, ANOVA F, and L1-logistic (or a comparable embedded method) \u2014 rather than reusing one selector for all conditions.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Method Implementation" |
| }, |
| { |
| "id": "ml15-code-injection", |
| "requirements": "The pipeline injects synthetic irrelevant features at one or more defined levels (including a no-injection baseline and a higher-injection case) and tracks which columns are injected so a noise-selection rate can be computed.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Experimental Setup" |
| }, |
| { |
| "id": "ml15-code-datasets", |
| "requirements": "The submission uses multiple datasets (sklearn built-ins or comparable) and a reasonable train/test split.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Dataset and Model Acquisition" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml15-exec", |
| "requirements": "Execution produces metrics across selectors and injection levels.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml15-exec-metrics", |
| "requirements": "Execution outputs a structured metrics artifact containing numeric test accuracy and a noise-selection-rate style measure for each implemented condition on at least one dataset with at least two injection levels.", |
| "weight": 16.6667, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml15-exec-seeds", |
| "requirements": "Reported metrics are aggregated over multiple random seeds per (dataset, condition, injection level) cell with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.", |
| "weight": 8.3333, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Hyperparameter Tuning" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml15-results", |
| "requirements": "Results address H1/H2/H3 directionally with a clear narrative.", |
| "weight": 3, |
| "sub_tasks": [ |
| { |
| "id": "ml15-result-h1", |
| "requirements": "The submission compares mean test accuracy at the higher injection level between the embedded L1-logistic method and each filter method per dataset and conveys whether L1 tends to be better \u2014 judge directionally against H1.", |
| "weight": 20.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml15-result-h2", |
| "requirements": "The submission reports the noise-selection rate for each method and conveys whether the embedded method selects fewer irrelevant features than the filter-method average on most datasets (H2).", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml15-result-h3", |
| "requirements": "The submission reports the accuracy drop from low-injection to high-injection for the embedded method and conveys whether the drop is small on most datasets (H3).", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml15-result-writeup", |
| "requirements": "The README or writeup describes the experimental setup, reports key metric tables, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations such as synthetic-noise realism, k-choice, classifier dependence, and seed/dataset scope. 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 |
| } |
|
|
