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DataRobot – Build Better Predictions Models

Posted by Marcus Zillman

DataRobot – Build Better Predictions Models

Building an accurate predictive model could mean combing through a near infinite combination of data transformations, features, algorithms and tuning parameters. DataRobot narrows down the search universe based on the characteristics of the training dataset and prediction target. It then executes only the most relevant end-to-end procedures for fitting a model (called Modeling Blueprints), to deliver the best predictive model in the fastest time possible. DataRobot uses cloud computing to cost-effectively evaluate thousands of Modeling Blueprints in parallel. It then systematically applies a cross-validation framework to accurately compare the performance of even the most diverse modeling techniques. Why DataRobot: a) Better Predictions – Benchmark your R and Python predictive models against those generated by DataRobot, or start using DataRobot models out-of-the-box; b) Faster Insights – Jumpstart your predictive modeling initiatives by immediately zooming in on the techniques and data sources that promise the best results; and c) Intuitive Modeling – Regardless of your level of experience, you can easily share, collaborate and manage the entire lifecycle of your models right from DataRobot. This will be added to Statistics Resources and Bog Data Subject Tracer™.

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