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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?
In this follow-up to Part 1, three techniques that take advantage of advanced modeling features and can help extract additional insight are reviewed:
- Stochastic gradient boosting
- Nonlinear regression splines
- Modeling automation
If you were not able to attend the webinar broadcast, you can still sign up and receive the recording and materials.
Included with registration:
- Webinar recording
- Data set
- Step-by-step instructions to replicate models
- Software download
Want to review Part 1? The recording, slides, tutorial, data set, and software are all available here.
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