tasks/ml/manifests/ML04.yaml

# ============================================================================
# T04 — Feature scaling choices for k-nearest neighbors under distribution shift
# ----------------------------------------------------------------------------
# Unlike paper_replication's P01-P07, the "synthesis" here frames a research
# QUESTION rather than a known paper's method. The model must design the
# experiment (conditions, metrics, datasets) — we only commit to what a
# competent study of this topic would include and what the rubric expects.
# ============================================================================

id: ML04
title: "Effect of standard, min-max, and robust scaling on KNN classification"
arxiv_id: null
venue: "ARC-Bench 2026"
paper_asset: null

# The "synthesis" plays the role of the upstream briefing: research question,
# background, why the question matters, what "a reasonable experiment" looks
# like. It deliberately does NOT pre-specify a single method to reproduce.
synthesis: |
  K-nearest neighbors (KNN) is highly sensitive to feature scale because
  distance calculations implicitly weight dimensions by their numeric ranges.
  In practice, preprocessing decisions such as StandardScaler, MinMaxScaler,
  or RobustScaler can alter neighborhood structure enough to change
  classification performance as much as model hyperparameters do. Despite this,
  scaling choice is often treated as a default rather than as an experimental
  factor.

  The core methodological question is not whether scaling helps versus no
  scaling (it usually does), but whether specific scaling families are better
  matched to particular data distributions. Standard scaling assumes roughly
  Gaussian-like behavior, min-max scaling compresses to bounded ranges and can
  be strongly affected by extrema, and robust scaling centers/scales by median
  and IQR to reduce outlier influence.

  A CPU-friendly, credible benchmark should compare these scalers with a fixed
  KNN classifier across multiple datasets that differ in outlier prevalence and
  marginal feature distributions, while averaging over multiple train/test
  splits. Because KNN is lightweight on small-to-medium sklearn datasets, the
  study can include both predictive quality and stability metrics without
  exceeding strict runtime constraints.

  The experiment should report test accuracy as the primary outcome, plus
  macro-F1 and split-to-split variability, and include at least one outlier-
  heavy synthetic dataset to stress robust scaling behavior. The goal is to
  produce falsifiable conclusions about when scaler choice materially changes
  KNN outcomes.

  *How does the choice among standard, min-max, and robust feature scaling change KNN classification performance and stability across datasets with different distribution and outlier characteristics?*

hypotheses:
  - id: H1
    statement: "RobustScaler + KNN achieves higher mean test accuracy than StandardScaler + KNN on the outlier-heavy synthetic dataset by at least 0.03 absolute accuracy, averaged over ≥5 random splits."
    measurable: true
  - id: H2
    statement: "Across the evaluated datasets, at least one real dataset shows an absolute test-accuracy gap of ≥0.02 between the best and worst of {StandardScaler, MinMaxScaler, RobustScaler}, indicating scaler choice materially affects KNN performance."
    measurable: true
  - id: H3
    statement: "No_scaling baseline is not the top-accuracy condition on at least 2 of 3 datasets when compared against the three scaling methods with the same KNN hyperparameters."
    measurable: true

experiment_design:
  research_question: "How does scaler choice (standard, min-max, robust) affect KNN classification accuracy and stability across datasets with different feature distributions and outlier profiles?"
  conditions:
    - name: "no_scaling_knn"
      description: "KNeighborsClassifier with fixed k and distance metric on raw features; control condition."
    - name: "standard_scaler_knn"
      description: "Pipeline(StandardScaler, KNeighborsClassifier) with the same KNN hyperparameters as control."
    - name: "minmax_scaler_knn"
      description: "Pipeline(MinMaxScaler, KNeighborsClassifier) with identical KNN settings."
    - name: "robust_scaler_knn"
      description: "Pipeline(RobustScaler, KNeighborsClassifier) with identical KNN settings."
  baselines:
    - "no_scaling_knn is the preprocessing-free baseline"
  metrics:
    - name: "test_accuracy"
      direction: "maximize"
      description: "Mean held-out accuracy over at least 5 random stratified splits per dataset."
    - name: "macro_f1"
      direction: "maximize"
      description: "Macro-averaged F1 on held-out data, averaged over splits."
    - name: "accuracy_std"
      direction: "minimize"
      description: "Standard deviation of test accuracy across random splits as a stability indicator."
  datasets:
    - name: "wine"
      source: "sklearn.datasets.load_wine"
    - name: "breast_cancer"
      source: "sklearn.datasets.load_breast_cancer"
    - name: "synthetic_outlier_classification"
      source: "sklearn.datasets.make_classification with injected feature outliers on a subset of samples"
  compute_requirements:
    gpu_required: false
    estimated_wall_clock_sec: 180

rubric_path: "experiments/arc_bench/config/ml/rubrics/ML04.json"