{
  "context": "Evaluates whether the solution leverages scikit-learn supervised learners to train, predict, and score binary classifiers per the spec, relying on scikit-learn APIs instead of manual implementations. Checks proper use of linear and ensemble estimators, probability outputs, and metrics via the library.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "Linear estimator",
      "description": "Implements the \"linear\" option with a scikit-learn linear classifier that supports probabilities (e.g., sklearn.linear_model.LogisticRegression or a LinearSVC wrapped in sklearn.calibration.CalibratedClassifierCV) and uses its fit/predict methods.",
      "max_score": 25
    },
    {
      "name": "Ensemble estimator",
      "description": "Implements the \"ensemble\" option with a scikit-learn tree-based ensemble classifier (e.g., sklearn.ensemble.RandomForestClassifier, GradientBoostingClassifier, or ExtraTreesClassifier), passing through random_state and using the estimator's fit/predict APIs.",
      "max_score": 25
    },
    {
      "name": "Probability outputs",
      "description": "Obtains class probabilities via the estimator or pipeline's predict_proba (or CalibratedClassifierCV.predict_proba when needed) rather than manual calculations, returning an (n_samples, 2) array in class order [0, 1].",
      "max_score": 20
    },
    {
      "name": "Accuracy scoring",
      "description": "Computes evaluation results with scikit-learn metrics such as sklearn.metrics.accuracy_score or the estimator's score method using provided test features and labels, avoiding hand-rolled accuracy loops.",
      "max_score": 15
    },
    {
      "name": "Model workflow",
      "description": "Returns scikit-learn estimators or pipelines that expose fit/predict/predict_proba, routing algorithm selection through those objects instead of custom algorithm code, and avoids reimplementing training logic outside scikit-learn.",
      "max_score": 15
    }
  ]
}