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  • Thursday, April 19, 2018
  • 3:40 PM–4:30 PM
  • North Hall 276

Laura Kapitula, Grand Valley State University

The goal of predictive modeling is to build a model or algorithm to estimate future occurrences of a target variable based on a set of input variables.  There are two main types of error to be concerned about in predictive modeling, one we call bias and one we call variance.  In this talk we will introduce basic ideas and terminology used in predictive modeling.  We will then discuss the problem of over-fitting, the variance bias trade-off and some modern methods for model selection.

 Refreshments precede the talk at 3:30 p.m. in NH 282.

April 2018
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