| { |
| "id": "ml09-root", |
| "requirements": "A credible experiment comparing Bayesian optimization, grid search, and random search for RandomForest hyperparameter tuning: methods are implemented with comparable budgets, executed on multiple datasets, and results are mapped directionally to H1/H2/H3.", |
| "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. If a well-motivated substitute (e.g., Hyperopt, Optuna, or a custom TPE-like surrogate) is used in place of a canonical Bayesian library, credit the scientific intent.", |
| "weight": 1, |
| "sub_tasks": [ |
| { |
| "id": "ml09-code", |
| "requirements": "The tuning strategies and shared evaluation setup are implemented correctly.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml09-code-strategies", |
| "requirements": "The submission implements distinct code paths for grid search, random search, and a Bayesian-style search for RandomForestClassifier (or equivalent) rather than reusing identical sampled configurations across methods.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Method Implementation" |
| }, |
| { |
| "id": "ml09-code-space", |
| "requirements": "A shared hyperparameter space is defined (covering several meaningful RF hyperparameters such as n_estimators, max_depth, min_samples_split, min_samples_leaf, max_features) so search methods are fairly comparable.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Hyperparameter Tuning" |
| }, |
| { |
| "id": "ml09-code-data", |
| "requirements": "The code loads multiple sklearn classification datasets (e.g., wine, breast_cancer, digits, or comparable) and uses a consistent train/test split plus CV protocol across search methods.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Dataset and Model Acquisition" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml09-exec", |
| "requirements": "The benchmark is executed and logs optimization quality and efficiency metrics.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml09-exec-metrics", |
| "requirements": "Execution outputs numeric best_cv_score and test_accuracy (or equivalents) for each implemented method on at least one dataset in a machine-readable metrics artifact.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml09-exec-budget", |
| "requirements": "Execution evidences budget control: non-default search methods evaluate a roughly comparable number of configurations, so quality comparisons are not confounded by wildly different evaluation counts.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Experimental Setup" |
| }, |
| { |
| "id": "ml09-exec-time", |
| "requirements": "Execution reports a wall-clock timing measure (e.g., search_time_sec) per method and dataset.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml09-results", |
| "requirements": "Results address H1/H2/H3 directionally and convey interpretable tradeoffs.", |
| "weight": 3, |
| "sub_tasks": [ |
| { |
| "id": "ml09-result-h1", |
| "requirements": "The submission compares Bayesian optimization vs random search on best_cv_score across the evaluated datasets and conveys whether Bayesian search is meaningfully better on most \u2014 judge directionally against H1.", |
| "weight": 20.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml09-result-h2", |
| "requirements": "The submission compares random search vs grid search on best_cv_score and conveys whether random is at least competitive with grid on most datasets (H2).", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml09-result-h3", |
| "requirements": "The submission checks test_accuracy differences between the best Bayesian-tuned and best grid-tuned models and conveys whether any practical gain (H3) is observed on at least one dataset.", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml09-result-writeup", |
| "requirements": "The README or writeup reports per-dataset metrics, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and discusses limitations such as small dataset set, budget tightness, surrogate instability, or CV variance. 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 |
| } |
|
|
