Feature selection is often the most time consuming part of developing a predictive model. With Orion Automated Feature Discovery, robust, predictive features can be discovered in under an hour.
Contact us for a demo of Orion's Automated Feature Discovery capabilities performed on your own dataset.
Orion has built in functionality that helps you rapidly uncover the most predictive features in your dataset and avoid common feature selection pitfalls.
Orion simultaneously tests hundreds of models with different feature specifications and identifies important features based on aggregated model fit.
Orion uses multilayered cross-validating techniques when training models. All fit statistics are generated on separate sets of 'testing' data which have not been used to generate the original model.
Orion's data-cleaning algorithms can deal with most common data problems, including: missing data, mis-coded feature types, differing feature magnitudes and messy string or text variables.
By training hundreds or even thousands of models we can find the models that work best - not just with individual features, but with multiple sets of interdependent features that work better together than separately in predicting outcomes.
Automated Feature Discovery enables you to bring predictive models to market faster, and with more confidence.
Obtain robust, comprehensive measures of feature importance that could be missed during the manual feature-selection process.
We accelerate the feature-selection process by testing hundreds of models with different feature specifications, and basing feature importance on aggregated model fit.
We use multilayered cross-validation techniques when training models: fit statistics (accuracy, etc) are generated on separate sets of 'testing' data independent of the original model.
Orion's automated suite of feature-engineering algorithms does not rely on specific domain knowledge to work.
Contact us to schedule a free demo of Orion's Automated Feature Discovery capabilities performed on a set of your own data.