se lect id , t1 . feature , zscore(t1.va lue , t2.mean,
t2.stddev) as score - - resca le(t1.va lue , t2.min , t2 .max) as
score f rom quantat ive t1 JO IN quantat ive_stats t2 ON (t1.
feature = t2. feature) where t1 .va lue i s not nu l l
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se lect feature , avg(weight) as we ight f rom (se lect adade
lta(features , ka iyaku) as ( feature , we ight) f rom t ra in_x3 )
t g roup by feature
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Feature Weight
Vip_Flg#T -18.5
point -12.3
Cancel_cnt 14.5
PV_PC 10.3
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S E L E C T t . k o k y a k u _ i d , s i g m o i d ( S U M ( m
. w e i g h t * t . v a l u e ) ) A S k a i y a k u _ p r o b , C A
S E W H E N s i g m o i d ( S U M ( m . w e i g h t * t . v a l u e
) ) > = 0 . 5 T H E N 1 E L S E 0 E N D A S p r e d i c t e d F
R O M m l _ o i s i x _ r e g u l a r _ c u s t o m e r _ c a n c e
l _ t e s t 0 3 _ e x p l o d ed _ f u k a w a 1 t L E F T O U T E
R J O I N a d a d e l t a _ m o d e l _ f u k a w a 1 m O N ( t . f
e a t u r e = m . f e a t u r e ) G R O U P B Y t . k o k y a k u _
i d
