Predictive modeling methods for continuous response variables. Focus on feature selection and building and validating predictive models based on regularized regression approaches. Topics may include: multiple regression, partial least-squares regression, ridge regression, lasso, elastic net, least-angle regression, random forests for regression, and support vector machines for regression.
Prerequisites: DATA 512 or permission of department chair.
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