How deeply does Financial AI in Practice cover population stability index?

Identifies PSI as a drift-monitoring metric but does not specify its calculation, thresholds, or response procedure.

Population stability index

As explained in Financial AI in Practice

The population stability index quantifies changes between distributions of a variable or model population across time or samples.

The population stability index (PSI) is a credit-scoring metric for monitoring drift in applicant populations. It helps flag shifts associated with changing customer demographics or economic conditions. section 4.4 section 4.4.1

PSI complements AUC and K-S: those metrics assess a model’s ability to distinguish good from bad borrowers, while PSI flags changes in the population. section 4.4.1

How Financial AI in Practice covers it

liveBook manning.com

Read Financial AI in Practice on liveBook Get the book at manning.com

Also explained in

liveBook manning.com

Machine Learning System DesignThe population stability index is a divergence measure that quantifies how a population's distribution differs from a reference distribution.

Compare how 2 books explain population stability index →

Related concepts

data driftmeasures · Data drift is a change over time in the statistical distribution or characteristics of data relative to a reference distribution or set of characteristics.
Evidently AImeasured by · Evidently AI is an open-source Python tool for evaluating, testing, monitoring, and reporting on machine-learning data, models, predictions, and behavior.
ScorecardMonitoringused by · ScorecardMonitoring is a scorecard-monitoring component that evaluates changes in score distributions and related statistical indicators.
Wasserstein distancealternative to · Wasserstein distance is a distance between probability distributions defined by the minimum cost of transporting probability mass between them under a specified ground distance.
model calibrationuses · Model calibration is the assessment or adjustment of a model’s outputs, scores, or confidence so they correspond more reliably to observed outcomes or a reference standard.

This explainer was generated by Manning's knowledge graph from the text of Financial AI in Practice. The section links point to the parts of the book it draws on.