Linear regression plays a big part in the everyday life of a data analyst, but the results aren’t always satisfactory. What if you could drastically improve prediction accuracy in your regression with a new model that handles missing values, interactions, AND nonlinearities in your data?
Instead of proceeding with a mediocre analysis, join us for this presentation, which will show you how MARS regression, TreeNet gradient boosting, and Random Forests can take your regression model to the next level with modern algorithms designed to expertly handle your modeling woes.
With these state-of-the-art techniques, you’ll boost model performance without stumbling over confusing coefficients or problematic p-values!
As a follow-up to the first webinar, we show you how to take these techniques even further to extract actionable insight and take advantage of advanced modeling features. Walk away with several different methods to turn your ordinary regression into an extraordinary regression!
o Stochastic gradient boosting: TreeNet plots show you the impact of every variable in your model; take it a step further by creating spline approximations to these variables and using them in a conventional linear regression for a boosted model performance!
o Nonlinear regression splines: MARS nonlinear regression will still give you what looks like a standard regression equation, but instead of coefficients, you’ll see transformations of your original variables.
o Modeling automation: learn how to cycle through numerous modeling scenarios automatically to discover best-fit parameters.