Logistic regression is a commonly used tool to analyze binary classification problems. However, logisitic regression still faces the limitations of detecting nonlinearities and interactions in data. In this webinar, you will learn more advanced and intuitive machine learning techniques that improve on standard logistic regression in accuracy and other aspects. As an APPLIED example, we will demonstrate using a banking dataset where we will predict future financial stress of a loan applicant in order to determine whether they should be granted a loan. Although the focus is related to finance and loans, the concepts are relevant for anyone who actively uses logistic regression and wishes to improve accuracy and predictor understanding.
Take a close look at CART to see the advantages of using train and test data when building your predictive models.
Work through large databases quickly and accurately with TreeNet Stochastic Gradient Boosting.
Combining CART decision trees and TreeNet Gradient Boosting for a winning combination.
Understand how CART grows a tree, what splitters, competitors and surrogates are, and why they are important when building a CART model.
Understand the value of PRIORS EQUAL and PRIORS DATA in common classification problems in CART.
Using CART For Transportation Operations - Video Presentation
Three Part Video Presentation on Using GUI and Command Line Together for Big Data
Learn: What is a surrogate? What makes a good surrogate? Why are surrogates important?
Discover what variable importance is and how it will help you build accurate predictive models.
This two-part series demonstrates the convenient facilities available for automation and command line processing in the Salford Predictive Modeler®.