Distribution Analytics¶
This tutorial adds descriptive numeric analytics and model-score analytics on
top of Getting started. Complete that tutorial first so
examples/demo has ingested aggregates.
The demo workspace configures two distribution processors:
ih_response_time(numeric_distribution) — t-digest of decision-to-outcome response times.ih_propensity_scores(score_distribution) — t-digests of propensity, final propensity, priority, and rank, split by positive/negative outcome for model-quality curves.
Query Numeric Distributions¶
95th-percentile response time by channel:
uv run valuestream query examples/demo VS_ResponseTime_P95 --by Channel --grain Day
Query Score Distributions¶
Median and P90 final propensity:
uv run valuestream query examples/demo VS_FinalPropensity_Median --by Channel --grain Day
uv run valuestream query examples/demo VS_FinalPropensity_P90 --by Channel --grain Day
Median priority:
uv run valuestream query examples/demo VS_Priority_Median --by Channel --grain Day
Query Model-Quality Metrics¶
ROC AUC reconstructed from the positive/negative propensity t-digests:
uv run valuestream query examples/demo ih_propensity_scores_roc_auc --by Channel --grain Day
How to Read These Numbers¶
Quantile metrics are computed from mergeable t-digest sketches, not raw rows, so they are approximate with bounded error; ROC AUC, average precision, and calibration are reconstructed from the score digests. Reports mark these values with approximation badges.
- Algorithms — sketch formulas, error behavior, and curve reconstruction.
- Processors —
numeric_distributionandscore_distributionstate layouts.