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DSCI 573 Feature and Model Selection

Performance of a classification model. Generalization error, overfitting of training data. Shrinkage, feature selection, Akaike Information Criterion, Bayesian Information Criterion. k-fold cross validation. Bootstrapping. Receiver Operating Characteristic curve. Elastic nets, regularization.

This course is not eligible for Credit/D/Fail grading.

Credits: 1

Pre-reqs: DSCI 571.


Status Section Activity Term Interval Days Start Time End Time Comments
RestrictedDSCI 573 001Lecture2 Tue Thu9:3011:00

Reserved for MDS-Vancouver and MDS-Computational Linguistic students.

FullDSCI 573 L01Laboratory2 Wed14:0016:00

Reserved for MDS-Vancouver students.

RestrictedDSCI 573 L02Laboratory2 Thu14:0016:00

Reserved for MDS-Vancouver students.

FullDSCI 573 L03Laboratory2 Tue15:3017:30

Reserved for MDS-Computational Linguistic students.