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.
You Will Learn
- When to apply supervised or unsupervised modeling methods
- Options for inserting machine learning into the decision making of your organization
- How to use multiple models for value estimation and classification
- How to properly prepare data for different kinds of supervised models
- Ways to interpret model coefficients and output to translate across platforms and languages, including the widely used Predictive Modeling Markup Language (PMML)
- Explore the pros and cons of “black box” models including ensembles
- How data preparation must be automated in parallel with the model if deployment is to succeed
- How to compare model accuracy scores to model propensity scores that drive decisions at deployment
- Analytics practitioners
- Data scientists
- IT professionals
- Technology planners
- Business analysts
- Analytics project leaders