| { |
| "id": "ml18-root", |
| "requirements": "A credible experiment studying post-hoc probability calibration methods (Platt, isotonic, temperature, or equivalents) for sklearn classifiers: calibration conditions are implemented, execution covers multiple datasets/classifiers with repeated 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 calibrator variants or alternative base classifiers that preserve the scientific question should be credited.", |
| "weight": 1, |
| "sub_tasks": [ |
| { |
| "id": "ml18-code", |
| "requirements": "Calibration methods and classifier setup are implemented correctly.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml18-code-calibrators", |
| "requirements": "The submission implements multiple calibration conditions \u2014 typically including uncalibrated, Platt, isotonic, and temperature scaling \u2014 as distinct code paths applied post-hoc to the same base model outputs.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Method Implementation" |
| }, |
| { |
| "id": "ml18-code-models", |
| "requirements": "The experiment includes multiple target classifiers (e.g., RandomForest, gradient boosting, SVM-RBF, or equivalents) with consistent train/calibration/test handling.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Experimental Setup" |
| }, |
| { |
| "id": "ml18-code-datasets", |
| "requirements": "The submission uses multiple datasets (sklearn built-ins or comparable) and an explicit calibration split separate from train and test.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Dataset and Model Acquisition" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml18-exec", |
| "requirements": "Execution reports calibration-focused metrics across conditions.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml18-exec-metrics", |
| "requirements": "Execution outputs numeric ECE, log loss, and test accuracy (or equivalents) for each implemented condition on at least one dataset-classifier pair in a machine-readable artifact.", |
| "weight": 16.6667, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml18-exec-seeds", |
| "requirements": "Metrics are aggregated over multiple random seeds per evaluated 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": "Evaluation, Metrics & Benchmarking" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml18-results", |
| "requirements": "Results quantitatively address H1/H2/H3 directionally with a clear narrative.", |
| "weight": 3, |
| "sub_tasks": [ |
| { |
| "id": "ml18-result-h1", |
| "requirements": "The submission computes relative ECE change versus uncalibrated and conveys whether at least one calibrator yields a meaningful ECE reduction across most classifier/dataset pairs \u2014 judge directionally against H1.", |
| "weight": 20.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml18-result-h2", |
| "requirements": "The submission compares isotonic vs Platt mean ECE per dataset and conveys the relative calibration quality plus any calibration-set size considerations (H2).", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml18-result-h3", |
| "requirements": "The submission reports accuracy deltas between calibrated and uncalibrated outputs per classifier-dataset pair and conveys whether accuracy changes are small (H3).", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml18-result-writeup", |
| "requirements": "The README or report conveys per-hypothesis outcomes (supported / refuted / inconclusive), cites key ECE/log-loss/accuracy numbers, and discusses limitations (dataset scope, binning sensitivity, seed count, calibration split size). 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 |
| } |
|
|
