Powerful methods for enhancing the performance of classification models. Topics will include random forests, boosting, bagging, model voting, propensity averaging, and segmentation models. Further topics may include support vector machines, graphical evaluation of classification models, feature selection, anomaly detection, and multiple imputation of missing data.
Prerequisites: DATA 513 or permission of department chair.
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