Platform Capabilities
A complete workflow that uses a shadow population estimator to uncover the true bias and variance decomposition - think of it as unlocking the Higgs field for machine learning
Detect instability before modeling begins
STABILITYLAB™ software evaluates data drift, feature instability, and sampling risk before models are trained—reducing downstream failure in production systems.
Select models that generalize, not just score well
FAMS™ software compares models using population-level bias and variance expectation to identify those machine learning functions with stable generalization behavior.
Optimize with guardrails, not guesswork
HYPERTUNE™ software applies staged hyperparameter optimization designed to improve robustness while avoiding variance-induced overfitting.
Translate analytics into management insight
MANAGEMENT IN A BOX™ software converts raw data into management-consulting style, decision-ready guidance for stakeholders and leadership teams.
Data stability diagnostics for production ML
STABILITYLAB™ software assesses data stability, feature sensitivity, and drift risk before and during model development—helping teams identify hidden failure modes early.
Importance-Stratified Generalization Gaps
ISGG is a custom metric that divides predictors into three strata: low, medium, and high importance. It then measures the ratio of importance to population variance to discover data that is inherently unstable.
Differential Feature Importance Stability
DFIS is a custom metric that asks and answers the question, 'do feature importances change across the population, and if so, is the model disrupted at those inflection points?'
Feature-Weighted Decomposition Divergence
FWDD enters the world of massive calculations to learn the importance and population variance of each predictor, expressed both visually and numerically so there are no surprises.
Model selection based on future stability, not single-run accuracy
FAMS™ software (Future-Aware Model Selection) evaluates predictive models across population expectations and perturbations to surface reliable generalization behavior.
The Shadow Population Estimator
Even with a perfect quantum computer, calculating the complete bias/variance equation to discover its population characteristics would take longer than the age of the universe - so we developed a method that takes minutes to deliver superb results.
Three Thoughtful Model Runs
Once the probability mass function is created from the population estimator, we select three data points of interest for modeling runs. The central run consists of up to 19 models for classification and 26 for regression, and all with a full set of metrics for achieving the right performance. Follow-on modelling runs consist of the top six algos taken from the main run.
GenAI chooses the best model for today and tomorrow
Armed with the probability mass function and three tables of model runs, GenAI can be used to find the one model that will perform best and remain longest in production. And the report explains in detail why this model is the best.
Hyperparameter optimization with guardrails
HYPERTUNE™ software applies staged tuning workflows designed to improve robustness while reducing overfitting risk.
The Global Environment
We all know that Bayesian hyperparameter tuning is very good, once you find the best neighborhood in the global environment.
That's where the custom recursive grid search, first developed at MIT for gradient boosted models, does its job. We have modified this to also work with CatBoost, AdaBoost, NGBoost, RandomForest, and ExtraTrees for a full complement of advanced algorithms.
The Local Schema
After the recursive grid search unlocks the neighborhood structure, standard Bayesian tuning is applied to adjust the performance even more, if possible.
Statistically Superior to Bayesian Alone
We know that in machine learning there is no free lunch, and this is also the case with the two-stage HYPERTUNE™ software.
According to our statistical analysis with classification problems, 60% of the time, HYPERTUNE™ will beat standard Bayesian by 2% in accuracy, whereas 40% of the time, Bayesian wins by 1%.
Another real advantage comes with tuning regression models, where the finalized performance can literally be outside of the population range - off the map.
Raw data translated into management guidance
MANAGEMENT IN A BOX™ software synthesizes analytical outputs into structured insights for decision-makers and non-technical stakeholders.
The Model
An Explainable Boosting Machine (EBM) is a supervised learning model designed to deliver near–state-of-the-art predictive performance while remaining fully interpretable.
Its core function is to model complex, non-linear relationships without sacrificing transparency, making it especially valuable in regulated or high-stakes settings.
The Plots
Global Explainability:
Which variables matter most?
How does risk change as X increases?
Where are thresholds, plateaus, or inflections?
Each feature has:
A standalone curve
A clear contribution to the prediction
Local Explainability:
Final score is the sum of visible feature contributions to the prediction
No post-hoc explanation tools required.
The Interpretation
Together with the top plots by importance, a multi-layer, bilateral prompt triggers the initial analysis by GenAI (currently ChatGPT 5.2).
Next comes the detailed plan for mitigating the problem, organized by importance or cost. Follow up questions are processed and more detailed planning in the result.
The democratization of management consulting is now.
