Predictor variable selection and dimensionality reduction for a predictive model

This describes a computer system that helps people build predictive models from historical data. It works by automatically sifting through many potential factors to identify and display only the most important ones that influence a specific outcome, thus simplifying the data before the model is built. The core invention focuses on this automated filtering and display of relevant variables.

Why it matters: Filed before the widespread adoption of powerful cloud computing and advanced machine learning frameworks. The methods for automated feature selection and guided model building described are now foundational to many accessible AutoML platforms, making implementation more feasible and impactful.

Status: Not in forceListed as no longer active. The specific reason is not in the record we hold.
How hard to build: SpecializedSoftware logic, machine learning algorithms, user interface

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