Description: This guide helps you create the metrics and storyboard used to interpret a High Performer Likelihood classification model in One Model. Building these assets typically requires coordination with your customer success team and data engineer to add the necessary code to your processing script so the model output tables and dimensions exist for metric and storyboard creation.
In most cases, if you are deploying another classification model using the same general output shape, you can reuse existing tables and dimensions and may not need additional data engineering work.
Module Type: Functional
Level: Intermediate-Advanced
Audience: Model & storyboard creators
Prerequisites: Access to and experience creating metrics & storyboards in One Model. "One AI Recipes", "High Performer Likelihood Model" & "Model Deployment" modules
Installation Instructions
There are a few recommended steps that should be completed in this order to get started building high performer likelihood metrics and storyboards:
Deploy a High Performer Likelihood classification model with SHAP enabled in the global settings.
Submit a ticket to have your data engineer add the required code to your processing script.
Allow your One Model site to reprocess.
Meet with your CS advisor to review the storyboard template structure and decide what you want to build and publish.
Note: Only create the metrics and storyboard for sharing model results at the individual-level if you have internal approval to do so.Using the metric guide, create the necessary metrics.
Using the storyboard guide, work with the CS team to create the pages/sections of interest.
Ensure storyboard viewers have the appropriate data access to review.
The One Model team is here if you get stuck!
High Performer Likelihood Metric Guide
Use the Metrics for High Performer Likelihood Models Google Sheet as the source of truth for metric definitions and calculations and to build metrics to visualize your model results. You are welcome to modify metrics for your organizational needs. Ensure you are only providing metric and storyboard permission to appropriate data access roles.
The standard High Performer Likelihood template usually requires metrics in a few categories:
Model scope & volume: who is scored and how many predictions exist for the latest run.
Model performance: metrics that make it easy to communicate how well the model identifies High Performer vs. Not High Performer outcomes.
Driver summaries: SHAP-based metrics that support “what’s influencing predictions” views at the overall level and, optionally, at the person level.
Likelihood distributions: metrics that bucket predictions into Low/Medium/High likelihood groups and summarize how those buckets distribute across the population.
Note: Many metrics are filtered to a specific augmentation. In your build, make sure the augmentation filter matches your High Performer Likelihood augmentation name.
Additionally, many metrics are filtered by label value. Model creators set these labels in the One AI Recipe Screen during the "Would you like to add more meaningful labels to the values you’re predicting?" step. In this guide, the label for employees predicted to be high performers is "High Performer," while the label for those not predicted to be high performers is "Not High Performer." You can customize these names to fit your organization's needs, but we recommend keeping label names consistent across models of the same recipe type to avoid needing new metrics for each model.
High Performer Likelihood Storyboard Template Descriptions
This section describes each section of the standard High Performer Likelihood storyboard and the value it provides. Your CS Advisor can show you this storyboard live upon request.
1. Model Information & Performance
What it is
A quick, executive-friendly snapshot of what the model predicts and how it’s behaving right now.
What you will typically see
A plain-language “About the model” statement describing the prediction frame (who is scored and the timeframe). Model context such as method, population size, number predicted high performer, prediction rate, features selected, and deployment/run details. A small performance view showing how well the model distinguishes High Performer vs. Not High Performer using common classification measures.
Why it matters
This is the “should we look deeper?” section. It sets expectations for how confident you should be in the predictions—especially for the High Performer class, which is often harder to predict.
2. Drivers and Directionality
What it is
A transparent view of what the model relied on most and whether those factors generally push predictions toward High Performer or toward Not High Performer.
What you will typically see
Ranked driver summaries for High Performer and for Not High Performer, plus a directionality view showing which features tend to increase high performance likelihood vs. decrease it in aggregate. This is typically powered by SHAP so viewers can see both impact and direction.
Why it matters
Stakeholders get understandable answers to “what signals is the model using?” Analysts can sanity-check whether the drivers align with business context and data quality expectations.
3. Where Does Likelihood Sit
What it is
A distribution view that shows how high performance likelihood is spread across the population and across key cuts.
What you will typically see
Employees grouped into Low / Medium / High likelihood buckets, plus distribution breakouts by selected dimensions (commonly things like pay grade, department, managerial status, generation).
Why it matters
It turns individual probabilities into an organizational story: Is potential broadly distributed or concentrated? Are there meaningful differences across levels or functions?
4. Forecasts
What it is
A forward-looking view of performance trends to support planning conversations.
What you will typically see
A performance trend over time extended into future periods (often showing expected percentage of high performers over time). This is intended to be directional and interpreted alongside organizational context.
Why it matters
It supports workforce planning and performance strategy conversations by helping teams anticipate shifts and decide where deeper analysis is needed.
5. By-name List(s)
What it is
A practical list view that lets approved audiences review who falls into higher (and/or lower) likelihood groups and add business context.
What you will typically see
A by-name list with employee identifiers and relevant context fields. In many templates, the list is designed to be filterable so leaders can narrow to their org or subgroup and review likely high performers with the right context.
Why it matters
This supports structured talent conversations: “who appears likely to be high performing,” “where do we have strength,” and “what might we want to validate further?”
6. Employee-level Analysis
What it is
A drill-down explainer for one employee at a time, intended for careful review with the right stakeholders, not self-service decisioning.
What you will typically see
Nothing appears until the viewer selects a single person from a Person (Predictions) filter. Once selected, the storyboard shows the employee’s predicted likelihood and explanation views: which features pushed the predictions to high and low performance, how the employee’s feature values compare to the model population average, and small performance rating history context block.
Why it matters
This is the “why did the model score this person this way?” section. It makes the prediction explainable and supports thoughtful review when appropriate.
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