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DTSTART:20180311T070000
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DTSTART:20171105T060000
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DTSTART;TZID=America/Toronto:20181031T130000
SEQUENCE:0
TRANSP:TRANSPARENT
DTEND;TZID=America/Toronto:20181031T150000
URL:https://uwaterloo.ca/statistical-consulting-survey-research-unit/events
 /introduction-feature-selection
LOCATION:M3 - Mathematics 3 200 University Avenue West 3127 Waterloo ON N2L
  3G1 Canada
SUMMARY:An introduction to feature selection
CLASS:PUBLIC
DESCRIPTION:Feature selection is the process of selecting a subset of relev
 ant\nfeatures (commonly known as predictors or independent variables) for\
 nmodel construction. Performing feature selection allows researchers to\ni
 dentify irrelevant data\, improve the interpretation and increase\npredict
 ive accuracy of learned models. A feature selection algorithm\ncan be seen
  as the combination of a search technique for proposing new\nfeature subse
 ts\, along with an evaluation which scores the different\nfeature subsets.
  The choice of evaluation measure heavily influences\nthe algorithm. There
  are three main categories of feature selection\nalgorithms: wrappers\, fi
 lters and embedded methods. In this seminar\,\nwe will introduce some basi
 c feature selection methods such as\nscore-based feature ranking\, stepwis
 e subset selection and LASSO\nregression.\n\nRegistration is free and ope
 n to all University of Waterloo faculty\,\nstaff\, graduate and undergradu
 ate students. The primary software we\nwill discussed in this seminar is R
 Studio. There is no hands-on work\nin this seminar.
DTSTAMP:20260414T045806Z
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