The data keeps coming.
Lots of variables.
Lots of data.
Lots of pressure.
What variables really matter?
What does it mean?
Can I trust my conclusions?
Life shouldn't be this hard.
And the data keeps coming ...
The fundamental issue is that most modeling approaches make a sequence of perfection assumptions to make the variable selection and modeling process tractable. Unfortunately, the real-world generally doesn't know, for example, that the variables have to be uncorrelated and independent or that the appropriate response behavior is a second-order polynomial with no cross-terms.
Our technology relaxes those assumptions — letting the data speak for itself — to automatically select the appropriate inputs and develop concise and insightful models. The developed models can even have trust metrics to identify when the model predictions shouldn't be trusted due entering new operating regions or fundamental changes in the targeted system. The model trustability can also be used in an active design-of-experiments mode to collect data to drive uncertainty out of the developed models.