Prepare Your Data
Encoding
Finally, It’s All Done for You – Automatically
Encoding of variables is a critical step in preparing analytical data sets that nobody should have to worry about. It’s also one of the most time-consuming aspects of predictive modeling.
With InfiniteInsight™ Explorer, your data is encoded automatically. You don’t have to give a second thought to whether a variable is nominal or ordinal, if it has missing values or contains outliers, if it’s highly correlated with another input variable. We make sure you always get optimal results.
- Optimized encoding for classification, regression, clustering, forecasting, etc.
- Automated processing of continuous, ordinal and nominal predictors
- Automated processing of missing values and outliers
- Automated binning and banding
- Automated processing of high cardinality nominal variables
- Selected InfiniteInsight™ for its automation of the most labor-intensive, high-maintenance and repetitive predictive analysis steps including data preparation, attribute importance, nominal attribute encoding, attribute value binning, combining bins, and selecting attributes (Sears)
- Significantly cut data preparation and able to handle more attributes than with traditional approaches (Vodafone Germany)
- InfiniteInsight™ prevents costly mistakes that occur during manual data preparation and variable reduction (Major marketing database service provider)
Kunter Kutluay, Head of Retail Credits
Success Stories
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“InfiniteInsight™ delivers the biggest gains in productivity and ultimately results.”
Dave Torgerson
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“InfiniteInsight™ is easy to use, creates very good models and is very, very fast - much faster than competing solutions.”
Kunter Kutluay
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“InfiniteInsight™ made predictive analytics fast and easy and has allowed us to realize results quickly”
Pradeep Kumar
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“With InfiniteInsight™ POLA has cut the time to build predictive models significantly”
Mitsuru Noda
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“InfiniteInsight's™ automated approach to modeling has made predictive analytics affordable, usable and understandable.”
Daniel Mathieux
