August 7, 2018
8:00 am - 5:30 pm
Duration: Full Day Course
Regression, decision trees, neural networks—along with many other supervised learning techniques—provide powerful predictive insights. New users of these established techniques are often impressed with how easy it all seems to be since automated model-building software is widely available. However, proper data preparation is necessary to get optimal results. No amount of software automation can make up for manual data prep. Many fail to even recognize that data prep was the problem. They likely conclude that the data was not capable of better performance.
In addition, though 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 half of its time on translating the business problem into a form the algorithms can assist with and preparing the data for optimal performance during modeling. The second half of the course focuses on proper model interpretation, looking for clues that the model will perform well on unseen data, and planning for deployment. While not hands-on, well-known and widely available modeling algorithms will be demonstrated and discussed.
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