{
  "context": "Evaluates whether the solution uses scikit-learn estimators with their unified fit/predict/transform contract to deliver the behaviors defined in the spec, including prediction, scaling-style transformation, combined invocation, and fit-state handling.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "Predictor fit/predict",
      "description": "Uses a scikit-learn predictive estimator such as LogisticRegression, DecisionTreeClassifier, or a comparable classifier; calls its fit on the training features and labels and predict on the evaluation features to produce the specified labels in order.",
      "max_score": 30
    },
    {
      "name": "Transformer fit/transform",
      "description": "Uses a scikit-learn transformer (e.g., StandardScaler or another transformer exposing fit/transform) to achieve column-wise zero mean and unit variance on the provided data, relying on fit then transform rather than manual normalization.",
      "max_score": 25
    },
    {
      "name": "Combined reuse",
      "description": "Implements the combined workflow by reusing the same fitted scikit-learn estimator instances (or a Pipeline) so that a single fit supports both predict and transform outputs without re-fitting or diverging parameters.",
      "max_score": 20
    },
    {
      "name": "Fit precondition",
      "description": "Detects unfitted estimators using scikit-learn conventions (e.g., sklearn.utils.validation.check_is_fitted or checking fitted attributes) and raises a clear error before predict/transform when fit has not been called.",
      "max_score": 15
    },
    {
      "name": "Argument consistency",
      "description": "Maintains the shared train/eval argument order across fit_predict, fit_transform, and fit_predict_transform entrypoints and forwards data directly into the scikit-learn methods without altering the expected shapes or ordering.",
      "max_score": 10
    }
  ]
}