Module scoring 

100-point model quality scoring system (spec §7) 100-Point Model Quality Scoring System (spec §7)

Evaluates models across seven dimensions based on data science and ML best practices, aligned with Toyota Way principles:

Dimension	Max Points	Toyota Way Principle
Accuracy & Performance	25	Kaizen (continuous improvement)
Generalization & Robustness	20	Jidoka (quality built-in)
Model Complexity	15	Muda elimination (waste reduction)
Documentation & Provenance	15	Genchi Genbutsu (go and see)
Reproducibility	15	Standardization
Security & Safety	10	Poka-yoke (error-proofing)

References

• [Raschka 2018] Model Evaluation, Model Selection, and Algorithm Selection in ML
• [Hastie et al. 2009] The Elements of Statistical Learning
• [Mitchell et al. 2019] Model Cards for Model Reporting
• [Gebru et al. 2021] Datasheets for Datasets
• [Pineau et al. 2021] ML Reproducibility Checklist

Structs

CriticalIssue

Critical issue that must be addressed

DimensionScore

Score for a single dimension

DimensionScores

Individual dimension scores

ModelFlags

Model feature flags

ModelMetadata

Model metadata for scoring

QualityScore

100-point model quality score

ScoreBreakdown

Breakdown item for dimension scoring

ScoringConfig

Configuration for quality scoring

TrainingInfo

Training information

Enums

Finding

Finding from quality analysis

Grade

Letter grade for quality score

ScoredModelType

Model type for scoring context

Severity

Critical issue severity

Functions

compute_quality_score

Compute quality score for a model