Advanced Predictive Diagnostics

Python-facing port of advanced-predictive-diagnostics.Rmd from the frozen R gp3bayes 0.5.0 reference. The statistical and governance framing below follows the canonical vignette; executable Python workflows use the mapped APIs listed later.

This article extends the prediction layer with diagnostics that remain descriptive rather than becoming automatic acceptance or rejection rules.

For a fitted model, posterior predictive discrepancy checks retain the entire replicated distribution:

Duration models additionally support predictive Q-Q and tail checks:

None of these diagnostics certifies adequacy automatically.

Python API mapping

• gp3bayespy.binary_calibration_error
• gp3bayespy.binary_confusion_table
• gp3bayespy.binary_precision_recall_curve
• gp3bayespy.binary_roc_curve
• gp3bayespy.duration_qq_table
• gp3bayespy.duration_tail_check
• gp3bayespy.plot_binary_precision_recall
• gp3bayespy.plot_binary_roc
• gp3bayespy.plot_duration_qq
• gp3bayespy.plot_duration_tail
• gp3bayespy.plot_ppc_statistic
• gp3bayespy.posterior_predictive_statistic
• gp3bayespy.ppc_statistic_table
• gp3bayespy.predict_model

Python usage

import gp3bayespy as gp

# All functions listed above are available from the package root.
# Use help(gp.<function>) or the API reference for the exact Python signature.

An executable workflow for this family is included in ../../examples/predictive_diagnostics.py.

Visual companion

These figures complement the canonical ported narrative and are generated from package functions.

Binary ROC curve. Deterministic visual companion generated from the Python package.

Binary calibration. Deterministic visual companion generated from the Python package.

Predictive residuals. Deterministic visual companion generated from the Python package.

The figures remain descriptive evidence and do not create automatic inferential decisions.