bffm-xgb

IPIP-BFFM Sparse Quantile Models

XGBoost quantile regression models for the 50-item IPIP Big-Five Factor Markers (BFFM) personality assessment, exported as ONNX for cross-platform inference.

What These Models Do

Each model takes up to 50 item responses (Likert 1--5) and predicts Big Five domain scores (Extraversion, Agreeableness, Conscientiousness, Emotional Stability, Intellect). The exported calibration regimes are fit for full 50-item completion and the primary domain-balanced 20-item sparse regime.

Key capability: sparse input. The models produce accurate predictions even when most items are unanswered (NaN). This allows fixed short-form assessments (as few as 20 items) without retraining or switching models.

How It Works

• 15 models in one graph -- 5 domains x 3 quantiles (q05, q50, q95), merged into a single ONNX file
• Structured sparsity augmentation -- training responses are masked into structured partial-response patterns (not uniform random dropout): focused buckets spanning 10-50 retained items (a 10-20-item target-assessment range, a 21-35-item transition range, and a 36-50-item near-complete range, each keeping a minimum number of items per domain), explicit injection of the Mini-IPIP 4-per-domain 20-item pattern, and roughly 15% imbalanced patterns that allow 0-item domains so the model also sees skewed coverage. Within each bucket the retained items are filled by information-rank-weighted sampling (items with higher cross-domain information are more likely to be kept). The deployed operating point is the domain-balanced 4-per-domain 20-item form
• Quantile regression -- pinball loss at tau = 0.05, 0.50, 0.95 provides median predictions with empirical 90% prediction intervals whose coverage is validated for the full_50 and sparse_20_balanced runtime regimes (raw quantile spreads; no post-hoc width adjustment is applied). Empirical 90% prediction-interval coverage is approximately 89.5% for the deployed domain-balanced 20-item form (held-out baseline evaluation), approximately 89.8% for the sparse_20_balanced runtime regime (validation under random balanced 20-item masking), and approximately 92.6% for the full_50 regime (validation)
• Norms-based percentiles -- raw predictions are converted to population percentiles using z-score norms fit on the training split only (n = 422,326); validation and test rows are held out so the norms do not leak into the percentile targets

Variants

The primary model is reference. Other variants are research ablations that isolate the contribution of each sparsity augmentation strategy.

Each variant directory contains:

• model.onnx -- merged ONNX model (5 domains x 3 quantiles)
• config.json -- runtime configuration, feature names, and norms
• README.md -- variant-specific model card with performance tables
• provenance.json -- full audit trail (git hash, data snapshot, training config)

Intended Use

Research and educational score-recovery of the 50-item IPIP-BFFM from partial responses (as few as 20 items), and reproducible study of sparse short-form scoring. Outputs are population percentiles with empirical 90% prediction intervals.

Out of Scope

Not validated for clinical diagnosis, employment or other high-stakes selection, or any individual consequential decision. The models recover a self-report questionnaire score, not an external personality trait.

Limitations

• Norms come from self-selected, anonymous online respondents (OSPP) and may not represent any general population; English-language IPIP items only.
• No demographic-subgroup or measurement-invariance analysis has been performed; accuracy may vary by gender, age, or region.
• No external / out-of-distribution validation: every reported number is on a held-out split of the same OSPP dataset, so these are score-recovery (recovering the full-scale score from a subset of its own items), not external-trait, metrics. The full-50 r ~ 1 is a score-recovery ceiling, not trait validity.
• The deployed 20-item Emotional Stability subscale is composed entirely of reverse-keyed items, so it is more sensitive to acquiescence and careless responding; the full 50-item assessment is unaffected.
• Accuracy degrades with fewer items; 20 items is the recommended minimum for reliable scoring.
• See each variant's README.md for per-model performance and the repository README.md / docs/research.md for the full limitations discussion.

Source Code

Training pipeline, evaluation scripts, and inference packages (Python + TypeScript): github.com/sprice/bffm-xgb

License

CC0 1.0 Universal -- Public Domain Dedication