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
LOOI
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.