Topics: Coronavirus | AI | Data Science | Deep Learning | Machine Learning | Python | R | Statistics

KDnuggets Home » News :: 2013 :: Aug :: Webcasts :: Salford: The Evolution of Regression Modeling: Watch on Demand ( 13:n21 )

Salford: The Evolution of Regression Modeling: Watch on Demand


This webinar series addresses the problems of classical regression and improvements such as regularized and nonlinear regression, and modern ensemble and data mining approaches. Very useful to any data miner or data scientist.



The Evolution of Regression ModelingThe Evolution of Regression Modeling: From Classical Linear Regression to Modern Ensembles
Watch On-Demand!

Instructor: Dan Steinberg, CEO and Founder

Class Description: Regression is one of the most popular modeling methods, but the classical approach has significant problems. This webinar series addresses these problems. Are you working with larger datasets? Is your data challenging? Does your data include missing values, nonlinear relationships, local patterns and interactions? This webinar series is for you! We will cover improvements to conventional and logistic regression, and will include a discussion of classical, regularized, and nonlinear regression, as well as modern ensemble and data mining approaches.

Part 1: Regression methods discussed

  • Classical Regression
  • Logistic Regression
  • Regularized Regression: GPS Generalized Path Seeker
  • Nonlinear Regression: MARS Regression Splines

Part 2: Hands-on demonstration of concepts discussed in Part 1

  • Step-by-step demonstration
  • Datasets and software available for download
  • Instructions for reproducing demo at your leisure
  • For the dedicated student: apply these methods to your own data (optional)

Part 3: Regression methods discussed
*Part 1 is a recommended pre-requisite

  • Nonlinear Ensemble Approaches: TreeNet Gradient Boosting; Random Forests; Gradient Boosting incorporating RF
  • Ensemble Post-Processing: ISLE; RuleLearner

Part 4: Hands-on demonstration of concepts discussed in part 3

  • Step-by-step demonstration
  • Datasets and software available for download
  • Instructions for reproducing demo at your leisure
  • For the dedicated student: apply these methods to your own data (optional)

Watch on demand here.


Sign Up

By subscribing you accept KDnuggets Privacy Policy