Effect of standard, min-max, and robust scaling on KNN classification
Domain: ml (machine-learning, data-preprocessing)
Topic: Analyzing the effect of feature scaling methods (standard, min-max, robust) on k-nearest neighbors classification performance across different data distributions
Venue: ARC-Bench 2026
Headline metric: test_accuracy (maximize)

Research briefing:
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:
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., 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., 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.

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: no
estimated_wall_clock_sec: 180

Requirements:

Scoring rubric:
requirements: A credible experiment studying how feature scaling (standard, min-max, robust, or none) affects KNN classification on datasets with different distributions: scaling conditions are implemented consistently, execution covers multiple datasets and random splits, and conclusions address H1/H2/H3 directionally.
judging_note: Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. If the submission uses alternative but well-motivated scalers or datasets that test the same scientific question, credit it accordingly.
sub_tasks: requirements: The scaling conditions and KNN setup are implemented in a way that supports a fair comparison.
sub_tasks: requirements: The submission implements the relevant scaling conditions — typically no-scaling, standard-scaler, min-max-scaler, and robust-scaler — as distinct code paths sharing the same KNN configuration.
task_category: Code Development
finegrained_task_category: Method Implementation, requirements: The submission uses multiple datasets including at least one real sklearn dataset and at least one synthetic/outlier-heavy dataset, with a reasonable train/test split.
task_category: Code Development
finegrained_task_category: Dataset and Model Acquisition, requirements: The implementation keeps KNN settings identical across scaling conditions and fits preprocessing only on training data within each split.
task_category: Code Development
finegrained_task_category: Experimental Setup, requirements: Execution produces benchmark metrics adequate to evaluate the hypotheses.
sub_tasks: requirements: Execution produces a machine-readable metrics artifact with numeric test accuracy and an additional metric such as macro-F1 for each evaluated (condition, dataset) cell.
task_category: Code Execution
finegrained_task_category: Evaluation, Metrics & Benchmarking, requirements: Reported metrics are aggregated over multiple random splits or seeds per (condition, dataset) with some dispersion measure (std or CI). More splits are better, but an honest small-split run with variance reported is preferable to a single split.
task_category: Code Execution
finegrained_task_category: Evaluation, Metrics & Benchmarking, requirements: The analysis evaluates all three hypotheses and presents interpretable findings.
sub_tasks: requirements: The submission compares robust-scaling vs standard-scaling + KNN on the outlier-heavy dataset and conveys whether robust-scaling shows a meaningful accuracy advantage. Exact percentage-point thresholds are not required.
task_category: Result Analysis
finegrained_task_category: Logging, Analysis & Presentation, requirements: The submission discusses whether scaler choice materially affects KNN accuracy on at least one real dataset — i.e., whether the best-vs-worst-scaler gap is non-trivial and of practical significance.
task_category: Result Analysis
finegrained_task_category: Evaluation, Metrics & Benchmarking, requirements: The submission compares the no-scaling baseline against the scaled conditions and conveys whether no-scaling tends to be clearly suboptimal across the evaluated datasets.
task_category: Result Analysis
finegrained_task_category: Logging, Analysis & Presentation, requirements: The README or writeup describes setup and datasets, reports the key metric values per condition, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations such as dataset scope, split count, or KNN-hyperparameter sensitivity. No strict word-count requirement.
task_category: Result Analysis
finegrained_task_category: Logging, Analysis & Presentation