[Forum SIS] Webinar Series: 3 Ways to Improve your Regression with Data Science and Machine Learning

[Lisa Solomon]

3 Ways to Improve your Regression models with Data Science and Machine Learning
(no charge)


Registration: http://hubs.ly/H01T4kR0

Alternative Link: http://info.salford-systems.com/3-ways-to-improve-your-regression-part1


January 20th and 27th, 10AM - 11AM PT
* If the time is inconvenient, please register and we will send you a recording.




ABSTRACT:
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 2-part webinar series.  We will show you how modern data science and machine learning algorithms can take your regression model to the next level and expertly handle your modeling woes.  You will walk away with several different methods to turn your ordinary regression into an extraordinary regression!

This webinar will be a step-by-step presentation that you can repeat on your own!
Included with Registration:

*         Webinar recording

*         30 day software evaluation

*         Dataset used in presentation

*         Step-by-step instruction for you to try at home

Who should attend:

*         Attend if you want to implement data science techniques even without a data science, statistical or programming background.

*         Attend if you want to understand why data science techniques are so important for forecasting.


Registration: http://hubs.ly/H01T4kR0

Alternative Link: http://info.salford-systems.com/3-ways-to-improve-your-regression-part1



Part 1: January 20 - We introduce MARS nonlinear regression, TreeNet gradient boosting, and Random Forests and show you how to extract actionable insight. Techniques:

*         Nonlinear regression splines (via MARS): this tool is ideal for users who prefer results in a form similar to traditional regression while allowing for bends, thresholds, and other departures from straight-line methods.

*          Stochastic gradient boosting (via TreeNet): this flexible and powerful data mining tool generates hundreds of decision trees in a sequential, error-correcting process to produce an extremely accurate model.

*         Random Forests: this method combines many decision trees independent of each other and is best suited in analyses of small to moderate datasets.


Part 2: January 27 - We will show you how to take these techniques even further and take advantage of advanced modeling features.
**There will be overlap with Part 1. It is recommended to watch Part 1, but not required. Techniques:

*         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!

*         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.

*         Modeling automation: learn how to cycle through numerous modeling scenarios automatically to discover best-fit parameters.

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