October 23, 2018
Duration: One Day Course
The Modeling Agency
Senior Consultant and Trainer
Regression, decision trees, neural networks—along with many other supervised learning techniques—provide powerful predictive insights. These data-driven insights inform the forces shaping your organization’s outcomes.
New users of these established techniques are often impressed with how easy it all seems. Software to build these models is widely available, but proper data preparation is necessary to get optimal results. No amount of software automation can make up for poor manual data prep. When projects fail, many won't even recognize that data prep was the problem. They will likely conclude that the data was not capable of better performance.
Additionally, although the predictive power of these machine learning models can be very impressive, there is no benefit unless they inform value-focused actions. Models must be deployed in an automated fashion to continually support decision making for residual impact. The instructor will show how to interpret supervised models with an eye toward decisioning automation. This course will demonstrate how real-world projects often combine different kinds of supervised models.
This one-day course will dedicate about half of its time on properly setting up and preparing the data for optimal performance during modeling, with the remainder spent on how to interpret supervised models with an eye toward decisioning automation
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