Modern Ensemble and Data Mining Approaches Part of the series: The Evolution of Regression from Classical Linear Regression to Modern Ensembles Hands-on, No charge Registration Link: All registrants will automatically receive access to recordings of earlier sessions which covered: Classical Regression, Logistic Regression, Regularized Regression, Nonlinear Regression, MARS Regression Splines Course Abstract: Overcoming Linear Regression Limitations 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! In our March 29th session we will focus on modern ensemble and data mining approaches. These methods dramatically improve the performance of weak learners such as regression trees. The techniques discussed here enhance the performance of regression trees considerably. These methods inherit the good features of trees (variable selection, missing data, mixed predictors) and improve on the weak features such as prediction performance. March 29, 10-11am PST - Regression methods discussed: Nonlinear Ensemble Approaches: TreeNet Gradient Boosting Random Forests Gradient Boosting incorporating Random Forests Ensemble Post-Processing: ISLE Importance Sampled Learning Ensembles RuleLearner rule based learning ensembles April 12,10-11am PST - 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) ---------------------------------------------- CLASS-L list. Instructions: