Module diagnostics 

Diagnostics for online models.

This module provides bounded-memory diagnostic primitives that help upper-layer applications answer:

• How many samples has the model seen?
• What is the recent error?
• Is the model still warming up?
• Is the model beating its baseline?
• Are the model parameters healthy (no NaN / Infinity)?
• What is a reasonable prediction interval?

Diagnostics are intentionally decoupled from the core model traits. A model implementation remains free to return a plain prediction; the diagnostic wrappers here layer on top without polluting the base API.

Re-exports

pub use baseline_comparator::BaselineComparator;

pub use baseline_comparator::ComparatorEntry;

pub use baseline_comparator::SwitchReason;

pub use model_health::ModelHealthReport;

pub use model_selector::OnlineModelSelector;

pub use model_selector::SelectorConfig;

pub use prediction_interval::PredictionInterval;

pub use prediction_interval::ResidualInterval;

pub use prediction_report::Confidence;

pub use prediction_report::PredictionReport;

pub use prediction_report::PredictionReporter;

pub use training_summary::TrainingSummary;

pub use training_summary::TrainingSummaryConfig;

pub use warmup::WarmupConfig;

pub use warmup::WarmupState;

pub use warmup::WarmupTracker;

Modules

baseline_comparator

Baseline comparison across multiple models.

model_health

Model parameter health checks.

model_selector

Online model selector with cooling period and minimum sample requirements.

prediction_interval

Prediction interval estimation.

prediction_report

Unified prediction report.

training_summary

Training summary statistics.

warmup

Warmup state tracking.