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 adaptive and 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
Sparsity augmentation -- during training, complete responses are randomly masked to simulate missing items, teaching the model to handle arbitrary missing-item patterns
Quantile regression -- pinball loss at tau = 0.05, 0.50, 0.95 provides median predictions with uncertainty bounds that are explicitly calibrated for full_50 and sparse_20_balanced runtime regimes
Norms-based percentiles -- raw predictions are converted to population percentiles using z-score norms derived from ~603k respondents

Variants

Variant	Description
`ablation_focused`	Research ablation variant
`ablation_none`	Research ablation variant
`ablation_stratified`	Research ablation variant
`reference`	Primary published model

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)

Source Code

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

License

CC0 1.0 Universal -- Public Domain Dedication

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