{
    "id": "ml08-root",
    "requirements": "A credible experiment studying imbalance-handling strategies (e.g., class weights, SMOTE, random over-/under-sampling) for binary classification: methods are implemented as distinct conditions, execution covers multiple datasets with multiple seeds, and results address H1/H2/H3 using balanced-accuracy-centered analysis.",
    "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. If SMOTE is implemented via a simplified interpolation recipe rather than the imblearn package, credit the scientific intent; alternative imbalance strategies that test the same question should also be credited.",
    "weight": 1,
    "sub_tasks": [
        {
            "id": "ml08-code",
            "requirements": "The imbalance-handling conditions are implemented in a way that supports a fair comparison under a shared classifier.",
            "weight": 2,
            "sub_tasks": [
                {
                    "id": "ml08-code-conditions",
                    "requirements": "The submission implements multiple imbalance-handling strategies \u2014 typically including a no-handling baseline, class-weight balancing, an oversampling variant (random or SMOTE-like), and an undersampling variant \u2014 as distinct code paths under a common binary classifier.",
                    "weight": 12.5,
                    "sub_tasks": [],
                    "task_category": "Code Development",
                    "finegrained_task_category": "Method Implementation"
                },
                {
                    "id": "ml08-code-smote",
                    "requirements": "If a SMOTE-style condition is claimed, synthetic minority samples are generated by interpolation between minority neighbors (not mere duplication) and applied only to training data. A simplified SMOTE-style implementation is acceptable if the intent is clear.",
                    "weight": 6.25,
                    "sub_tasks": [],
                    "task_category": "Code Development",
                    "finegrained_task_category": "Method Implementation"
                },
                {
                    "id": "ml08-code-data",
                    "requirements": "The submission uses multiple datasets (loaded or synthesized via sklearn utilities) and enforces an imbalanced binary class distribution in training data.",
                    "weight": 6.25,
                    "sub_tasks": [],
                    "task_category": "Code Development",
                    "finegrained_task_category": "Dataset and Model Acquisition"
                }
            ],
            "task_category": null,
            "finegrained_task_category": null
        },
        {
            "id": "ml08-exec",
            "requirements": "Execution reports imbalance-aware metrics per condition with reasonable seed coverage.",
            "weight": 2,
            "sub_tasks": [
                {
                    "id": "ml08-exec-metrics",
                    "requirements": "Execution produces a machine-readable metrics artifact with numeric balanced-accuracy and at least one of {minority recall, average precision, F1} per implemented condition on at least one dataset.",
                    "weight": 16.6667,
                    "sub_tasks": [],
                    "task_category": "Code Execution",
                    "finegrained_task_category": "Evaluation, Metrics & Benchmarking"
                },
                {
                    "id": "ml08-exec-seeds",
                    "requirements": "Reported metrics are aggregated over multiple random seeds per (dataset, condition) with some dispersion measure. More seeds are better, but honest small-seed runs with variance reported are preferable to single deterministic runs.",
                    "weight": 8.3333,
                    "sub_tasks": [],
                    "task_category": "Code Execution",
                    "finegrained_task_category": "Evaluation, Metrics & Benchmarking"
                }
            ],
            "task_category": null,
            "finegrained_task_category": null
        },
        {
            "id": "ml08-results",
            "requirements": "The analysis addresses H1/H2/H3 directionally with a clear narrative.",
            "weight": 3,
            "sub_tasks": [
                {
                    "id": "ml08-result-h1",
                    "requirements": "The submission compares no-handling against class-weighting and/or SMOTE-style oversampling on balanced-accuracy and conveys whether imbalance handling yields a meaningful improvement \u2014 judge directionally against H1.",
                    "weight": 25.0,
                    "sub_tasks": [],
                    "task_category": "Result Analysis",
                    "finegrained_task_category": "Logging, Analysis & Presentation"
                },
                {
                    "id": "ml08-result-h2h3",
                    "requirements": "The submission reports the minority-recall / average-precision comparisons needed for H2 and the random-oversampling vs class-weighting comparison needed for H3, conveying qualitative outcomes for both.",
                    "weight": 12.5,
                    "sub_tasks": [],
                    "task_category": "Result Analysis",
                    "finegrained_task_category": "Logging, Analysis & Presentation"
                },
                {
                    "id": "ml08-result-writeup",
                    "requirements": "The README or writeup describes setup, reports per-dataset metric outcomes, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations such as synthetic-dataset realism, threshold dependence, seed count, or SMOTE simplifications. 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
}