A few things we’re great at

Hyper-flat Data Sets

Addressing 21st-century challenges, our solutions combat the risk of false correlations in data sets with more columns than rows.

Our cutting-edge Feature Importance and ML Confidence algorithms ensure fair decision-making by detecting and preventing bias at every stage of the process.

Bias Detection

Analysis of Causality

Directly selecting key features from data, our method establishes a hierarchy of predictive elements, unveiling crucial relationships.

Enhance predictions with minimal data. Eliminate noise, construct an optimal minimal dataset to boost predictor performance, and cut down on data collection costs.

Dimensionality Reduction

Feature Importance

At the predictor level, Feature Importance tells what data is most valuable. At the individual level, it turns a predictor into a diagnostic tool.

PolyML specializes in maximizing savings through expert handling of machine and process-generated data.

Continuous Data from Machines and Processes


Methods that assign a numerical value to each of the features selected. This value is proportional to the predictive power of the feature.

Proxies may include the Cause in them, or an SME will be able to determine it by analyzing them.