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Data Driven Fitting with MATLAB

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Richard Willey, MathWorks

In this webinar, you will learn how to do data driven fitting with MathWorks products.
Data driven fitting derives all of the information required to generate a model directly from the data set.  Data driven fitting is also referred to as “black box” modeling and nonparametric fitting.

Applied examples from the webinar include: 

  • Comparing the accuracy of different fitting techniques
  • Using back-testing to measure the accuracy of your model
  • Generating confidence intervals with bootstrap
  • Using cross validation to avoid overfitting

Key analytical techniques include:

  • Neural Networks
  • Boosted and bagged decision trees
  • Localized regression
  • Smoothing splines

View the MATLAB code

About the Presenter: Richard Willey is a product marketing manager focused on MATLAB and add-on products for data analysis, statistics, and curve fitting. Prior to joining MathWorks in 2007, Richard worked at Wind River Systems and Symantec. Richard has a dual masters in engineering and management from the Massachusetts Institute of Technology and a master’s degree in economics from Indiana University.

Product Focus

  • Curve Fitting Toolbox
  • Neural Network Toolbox
  • Statistics and Machine Learning Toolbox

Recorded: 14 Aug 2012