5-classification-DA

# 3 prior probabilities 30 k02ak ldadolivedolive11c67

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Unformatted text preview: 0 pool ¯ ¯ ¯ ¯￿ ¯ − X1 Spooled X1 ≥ X2 Spooled X0 − X2 S−1 ed X2 pool !"#\$%&'()*:+(##";,*X0*)=*@%=2A*<")?*+(%@.#)*ck Statistics all Spring to ISU see that in general the rule is to503, ocate 2013,the group which has the larges ¯ ￿ −1 Xk Spooled X0 − ¯￿ ¯ Xk S−1 ed Xk pool 16 k = 1, ..., g 4 456*#%1/\$)"%\$ d the linear discriminant functions. orating-!/*\$(+\$2+().#*)?"#*D2(')"),0*\$(++.3*)?.*+"'.(%* prior probabilities 3"#\$%"&"'(')0* k0**;=%*@%=2A*k bability for group k is pkcthen the discriminant functions become ¯￿ ¯￿ ¯ Xk S−1 ed X0 − Xk S−1 ed Xk + log pk pool pool k = 1, ..., g e natual log. This shifts the boundary away from the group with the larger probability e might use the sample size ingroup k e oils, nPrior = 98, nN th = 151 to ass the oliv S ar d Sample mean for 98/249 = 0.39, pN th = 151/249 = 0.61. The result is to change the constant 04 + log(151/Inverse pooled ing error ivariance better, 7/249 = 0.028. 98)). The train sample s slightly for group k New observation to be predicted !"#\$%&'()*:+(##";,*X0*)=*@%=2A*<")?*+(%@.#)*ck Statistics 503, Spring 2013, ISU 16 ¯ − nd ¯ ¯ ws the classiﬁcation￿ bou1 ed X0 −or l￿inear regression,= 1, ..., t neighbors and LD Xk Spool aries f Xk S−1 ed Xk k neares g pool of these methods are: linear regression 7/249 = 0.028, nearest neighbors 3/24 d the linear discriminant functions. . used to compute LDA classiﬁcation rule is: 456*#%1/\$)"%\$ orating-!/*\$(+\$2+().#*)?"#*D2(')"),0*\$(++.3*)?.*+"'.(%* prior probabilities 3"#\$%"&"'(')0* k0**;=%*@%=2A*k -lda(d.olive[d.olive[,1]!=1,c(6,7)], bability for group k is pkcthen the discriminant functions become live[,1]!=1,1],prior=c(0.5,0.5)) ¯￿ ¯￿ ¯ [d.olive[,1]!=1,1],X0 − Xk S−1 ed Xk + log pk k = 1, ..., g Xk S−1 ed pool pool mple1.lda,d.olive[d.olive[,1]!=1,c(6,7)],dimen=1)\$class) e natual log. This shifts the boundary away from the group with the larger probability e might use the sample size in the olive oils, nPrior = 98, nN th = 151 to ass S ar d than two groups 98/249 = 0.39, pN th = 151/249 = 0.61. The result is to change the constant 04 o groups, /Inverse sipooled ing error iequationy2:for group k = 0.028. 98)). The train sample s slightl tw + log(151 ﬁrst con der rearranging variance better, 7/249 New observation to ￿be−1 predicted ￿ −1 ¯￿ X1 S−1 ed X0 pool ¯ ¯ ¯ ¯￿ ¯ − X1 Spooled X1 ≥ X2 Spooled X0 − X2 S−1 ed X2 pool !"#\$%&'()*:+(##";,*X0*)=*@%=2A*<")?*+(%@.#)*ck Statistics all Spring to ISU see that in general the rule is to503, ocate 2013,the group which has the larges ¯ ￿ −1 Xk Spooled X0 − ¯￿ ¯ Xk S−1 ed Xk pool 16 k = 1, ..., g 4 456*#%1/\$)"%\$ d the linear discriminant functions. orating-!/*\$(+\$2+().#*)?"#*D2(')"),0*\$(++.3*)?.*+"'.(%* prior probabilities 3"#\$%&q...
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## This note was uploaded on 02/06/2014 for the course STAT 503 taught by Professor Staff during the Fall '08 term at Iowa State.

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