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3 Ways to Improve your Regression with Data Science and Machine Learning Pa= rt 2=20 =A0(no charge, Case Study, Step-by-step, Hands-on option) Registration: http://hubs.ly/H01Y9Fr0 Alternative Link: http://info.salford-systems.com/3-ways-to-improve-your-re= gression-part2 January 27th, 10AM - 11AM PT * If the time is inconvenient, please register and we will send you a recor= ding * Part 1 is not required, to understand the approach and concepts in tomorr= ow's webinar; but, if you want a refresher, you can see last week's webinar= at your convenience. =A0Link to recording of Part 1: http://hubs.ly/H01Q4b= F0 ABSTRACT: Last week, we showed you how you could drastically improve predi= ction accuracy in your linear =A0regression with a new model that handles m= issing values, interactions, AND nonlinearities in your data. =A0As a follo= w-up to the last week's webinar, we will show you how to take data science = techniques even further to extract actionable insight and take advantage of= advanced modeling features. You will walk away with several different meth= ods to turn your ordinary regression into an extraordinary regression! Techniques used: *Stochastic gradient boosting: TreeNet plots show you the impact of every v= ariable in your model; take it a step further by creating spline approximat= ions 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 y= ou what looks like a standard regression equation, but instead of coefficie= nts, you'll see transformations of your original variables. * Modeling automation: learn how to cycle through numerous modeling scenari= os automatically to discover best-fit parameters. Included with Registration: * On-demand recording of webinar * Data set used in presentation * Step-by-step instructions * 30-day free access to MARS, TreeNet, and Random Forests More details: * Last week, we showed you how you could drastically improve prediction acc= uracy in your linear =A0regression with a new model that handles missing va= lues, interactions, AND nonlinearities in your data. =A0This week, we will = rebuild these original models and get straight to the more advanced feature= s.=A0=20 * We will quickly review how to incorporate nonlinearities in a regression = splines model=A0 AND THEN show you how to automatically detect interactions= and include these to lead to an even better result. * We will quickly review stochastic gradient boosting, and how, with plots = you can see how each variable contributes to your model.=A0=A0 And then, th= is week you will see how to create approximations from these plots and use = these in a standard linear regression as your inputs. * We will also explore the benefits of model automation. Without any custom= programming, you can quickly cycle through different modeling scenarios, s= uch as intelligently decreasing your predictor pool by removing variables o= ne by one, or automatically re-running your regression model using differen= t loss functions. This gives you the option to create many different models= and choose the best for your analysis needs. These techniques are great for skeptics who like to stick with standard reg= ression but wish to see dramatic improvements. With very large datasets, yo= u will see a significant speed benefit as well.=A0 Learn what is being used= at some of the largest banks and credit companies in the world. And if you want a refresher, you can see last week's webinar at your conven= ience: http://hubs.ly/H01Q4bF0 Who should attend:=20 * Attend if you want to implement data science techniques even without a da= ta science, programming, or even a statistical background. * Attend if you want to understand why data science techniques are so impor= tant for analysts. Registration: http://hubs.ly/H01Y9Fr0 Alternative Link: http://info.salford-systems.com/3-ways-to-improve-your-re= gression-part2
