`nexyron.model_gain_table`

Generic Description

nexyron.model_gain_table build a rank-based gain table for one registered native model on a labeled abstraction dataset

CALL nexyron.procedures()
YIELD name, description, parameters, output_columns
WHERE name = 'nexyron.model_gain_table'
RETURN name, description, parameters, output_columns

Semantic model procedures score, evaluate, compare, explain, materialize, and inspect registered models that are bound to named datasets or snapshots.

Consumer-Level Explanation

This procedure belongs to the model and feature-serving runtime. Use it when you need feature resolution, scoring, explanation, evaluation, or model-quality analysis from already registered abstraction contracts.

Parameters

• name: Model name Required.
• dataset: Dataset name Required.
• split: Optional split label filter such as train, validation, or test Optional.
• bin_count: Optional number of ranked bins to build. Defaults to 10 Optional.
• at: Optional as-of timestamp in epoch milliseconds for bound dataset execution Optional.
• system_at: Optional system-time cutoff in epoch milliseconds for dual-time replay Optional.
• prefer_materialized: When true, reuse a matching materialized dataset before rebuilding it Optional.

Output Columns

• model
• dataset
• split
• bin_index
• bin_count
• row_count
• positive_count
• positive_rate
• avg_prediction
• cumulative_row_count
• cumulative_positive_count
• cumulative_capture_rate
• source
• model_version

Example Contract Prerequisite

The executable Cypher blocks on this page query nexyron.procedures() so they work in an empty database and stay synchronized with the live procedure registry. A direct CALL nexyron.model_gain_table(...) requires the named abstraction contracts, artifacts, features, snapshots, datasets, models, or policies referenced by that call to exist first; otherwise the runtime correctly fails with an unknown-contract error rather than inventing state.

Conceptual Explanation

The important thing about nexyron.model_gain_table is that it stays inside the same Cypher and abstraction-contract runtime as the rest of Nexyron. That means it can compose with named abstractions, time-aware snapshot contracts, document-style payloads, graph-native identity, and later model or policy layers without forcing you to move into a separate tool first.

More Detailed Explanation

In practical queries, start by deciding the row grain you want after the call: one row per node, one row per path, one row per registry object, one row per artifact, or one row per summary. Then keep that grain explicit with YIELD and named projections. That is the difference between a useful planner-facing example and a vague call that downstream tooling cannot safely compose. For contract-driven abstraction procedures, the executable examples on these pages intentionally inspect procedure metadata unless the required named artifacts are created in the same example.

Advanced Example

CALL nexyron.procedures()
YIELD name, parameters, output_columns
WHERE name = 'nexyron.model_gain_table'
RETURN name,
       [p IN parameters | p.name] AS parameter_names,
       output_columns
ORDER BY name

Real Use Cases

• Evaluate, compare, and operationalize retention-risk models directly against named abstraction datasets before moving a candidate model into campaign or service decisioning.
• Score live member cohorts from the same feature contract used offline so risk review, calibration review, and production serving stay on one contract path.

Real Limitations And Tradeoffs

• This surface is contract-driven. If the registered feature, snapshot, dataset, model, or policy definition is weak, the procedure will faithfully execute that weak contract rather than silently repairing it.
• Reported quality or causal metrics are only as defensible as the dataset, split, and temporal contract supplied underneath.