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Tips & Tricks for Improving Your Logistic Regression

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.

TreeNet® for Beginners

Work through large databases quickly and accurately with TreeNet Stochastic Gradient Boosting.

Using CART® For Beginners

Learn how to grow CART trees, view tree details, understand CART's color–coding mechanism and print your results.

Working With SPM® Command Line

This two-part series demonstrates the convenient facilities available for automation and command line processing in the Salford Predictive Modeler®.

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