Regression, decision trees, neural networks – along with many other supervised learning techniques, provide powerful predictive insights. These data-driven insights inform which forces are shaping your organization’s outcomes. Once built, the models can produce key indicatorsto optimize the allocation of organizational resources.
New users of these established techniques are often impressed with how easy it all seems to be. Modeling software to build these models is widely available. However, proper data preparation is necessary to get optimal results. No amount of software automation can make up for poor 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. This one-day course will dedicate about half of its time on properly setting up and preparing the data for optimal performance during modeling.
The deployment phase includes proper model interpretation and looking for clues that the model will perform well on unseen data. 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.
You Will Learn
- When to apply supervised or unsupervised modeling methods
- Options forinserting machine learninginto 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
- Interpret model coefficients and output to translateacross platformsand 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
- Compare model accuracy scores to model propensity scores that drive decisions at deployment
- Analytic Practitioners; Data Scientists; IT Professionals; Technology Planners; Consultants; Business Analysts; Analytic Project Leaders