BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Drupal iCal API//EN
X-WR-CALNAME:Events items teaser
BEGIN:VEVENT
UID:63f05a02d39a0
DTSTART;TZID=America/Toronto:20181031T130000
SEQUENCE:0
TRANSP:TRANSPARENT
DTEND;TZID=America/Toronto:20181031T150000
SUMMARY:An introduction to feature selection
CLASS:PUBLIC
DESCRIPTION:Summary \n\nFeature selection is the process of selecting a sub
 set of relevant\nfeatures (commonly known as predictors or independent var
 iables) for\nmodel construction. Performing feature selection allows resea
 rchers to\nidentify irrelevant data\, improve the interpretation and incre
 ase\npredictive accuracy of learned models. A feature selection algorithm\
 ncan be seen as the combination of a search technique for proposing new\nf
 eature subsets\, along with an evaluation which scores the different\nfeat
 ure subsets. The choice of evaluation measure heavily influences\nthe algo
 rithm. There are three main categories of feature selection\nalgorithms: w
 rappers\, filters and embedded methods. In this seminar\,\nwe will introdu
 ce some basic feature selection methods such as\nscore-based feature ranki
 ng\, stepwise subset selection and LASSO\nregression.\n\nRegistration\n[/s
 tatistical-consulting-and-collaborative-research-unit/node/45] is\nfree an
 d open to all University of Waterloo faculty\, staff\, graduate\nand under
 graduate students. The primary software we will discussed in\nthis seminar
  is RStudio. There is no hands-on work in this seminar.\n 
DTSTAMP:20230218T045426Z
END:VEVENT
END:VCALENDAR