3 Ways to Improve your Regression models with Data Science and Machine Learning
(no charge)
Registration (includes recording): http://hubs.ly/H01VzrN0
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 an
important addition to classical statistical approaches.
Registration: http://hubs.ly/H01VzrN0
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.
