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t
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l J
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Art
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J
-
AI
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Vo
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13
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Dece
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a
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Ins
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d
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m
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tal
in
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ica
to
rs.
T
h
u
s,
th
is
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u
d
y
a
ims
t
o
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v
a
lu
a
te
th
e
e
ffica
c
y
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f
m
a
c
h
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e
lea
rn
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n
g
in
q
u
a
li
ty
-
of
-
li
fe
p
re
d
icti
o
n
b
a
se
d
o
n
p
ro
p
e
rty
c
rime
a
n
d
tem
p
e
ra
tu
re
.
F
iv
e
m
a
c
h
in
e
lea
rn
i
n
g
a
l
g
o
ri
th
m
s we
re
u
se
d
to
b
e
e
m
p
iri
c
a
ll
y
c
o
m
p
a
re
d
n
a
m
e
l
y
g
e
n
e
ra
li
z
e
d
li
n
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a
r
m
o
d
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l
(G
LM
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n
d
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m
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e
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tree
(DT)
,
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ra
d
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t
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d
tree
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BT)
a
n
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su
p
p
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rt
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m
a
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h
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(S
VM)
a
re
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m
p
a
re
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e
m
p
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a
ll
y
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e
p
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rm
a
n
c
e
o
f
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a
c
h
m
a
c
h
in
e
lea
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g
a
lg
o
rit
h
m
in
p
re
d
ictin
g
th
e
q
u
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li
t
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f
li
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a
s
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se
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b
a
se
d
o
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th
e
a
tt
ri
b
u
tes
o
f
p
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o
p
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rt
y
c
rime
a
n
d
tr
o
p
ica
l
c
li
m
a
te
(tem
p
e
ra
tu
re
).
De
sp
it
e
in
it
ial
l
o
w
c
o
rre
lati
o
n
wi
th
q
u
a
li
t
y
o
f
li
fe
,
tem
p
e
ra
tu
re
si
g
n
ifi
c
a
n
tl
y
c
o
n
tri
b
u
tes
to
sp
e
c
ifi
c
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l
g
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th
m
s,
e
n
h
a
n
c
in
g
p
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d
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v
e
a
c
c
u
ra
c
y
.
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h
is
sh
o
ws
th
e
c
o
m
p
lex
it
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f
m
a
c
h
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rn
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g
imp
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c
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e
m
e
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s
a
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st
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g
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l
g
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rit
h
m
,
f
o
ll
o
we
d
b
y
RF
a
n
d
DT
.
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e
fin
d
in
g
s
h
ig
h
li
g
h
t
th
e
imp
o
r
tan
c
e
o
f
se
e
m
in
g
l
y
u
n
r
e
late
d
fa
c
to
rs
in
p
re
d
icti
o
n
o
u
tco
m
e
s.
Th
is
p
a
p
e
r
p
re
se
n
ts
a
fu
n
d
a
m
e
n
tal
re
se
a
rc
h
fra
m
e
wo
rk
u
se
fu
l
fo
r
h
e
lp
i
n
g
e
d
u
c
a
to
rs
a
n
d
re
se
a
rc
h
e
rs
to
e
x
p
lo
re
in
d
e
p
th
q
u
a
li
t
y
o
f
li
fe
p
re
d
icti
o
n
with
u
si
n
g
p
ro
p
e
rty
c
rime
a
n
d
tem
p
e
ra
tu
re
a
s a
fa
c
to
r.
K
ey
w
o
r
d
s
:
Ma
ch
in
e
lear
n
in
g
Pre
d
ictio
n
Pro
p
er
ty
cr
im
e
Qu
ality
o
f
life
T
r
o
p
ical
clim
ate
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Sit
i Ra
s
id
ah
M
d
Sak
ip
Dep
ar
tm
en
t o
f
B
u
ilt E
n
v
ir
o
n
m
en
t a
n
d
T
ec
h
n
o
l
o
g
y
,
Un
iv
er
s
iti T
ek
n
o
lo
g
i M
AR
A
Per
ak
B
r
an
ch
,
Ser
i I
s
k
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d
ar
C
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p
u
s
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Ma
lay
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E
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ir
7
0
4
@
u
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u
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m
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1.
I
NT
RO
D
UCT
I
O
N
T
h
e
co
n
ce
p
t o
f
p
r
ed
ictin
g
q
u
al
ity
o
f
life
h
as b
ee
n
e
x
ten
s
iv
ely
ex
p
lo
r
ed
in
v
ar
i
o
u
s
s
tu
d
ies an
d
p
ap
er
s
,
u
tili
zin
g
d
if
f
er
e
n
t
m
eth
o
d
s
a
n
d
in
d
icato
r
s
.
C
o
m
m
o
n
s
tr
at
eg
ies
in
v
o
lv
e
ev
alu
atin
g
h
ea
lth
s
tatu
s
[
1
]
–
[
3
]
,
s
o
cio
ec
o
n
o
m
ic
elem
e
n
ts
[
2
]
,
s
u
b
jectiv
e
well
-
b
ein
g
[
4
]
,
[
5
]
,
an
d
en
v
ir
o
n
m
e
n
tal
co
n
d
itio
n
s
[
6
]
,
[
7
]
to
f
o
r
ec
ast
an
d
ass
ess
q
u
ality
o
f
life
.
Ho
wev
er
,
in
th
e
r
ea
lm
o
f
u
r
b
an
g
eo
g
r
ap
h
y
an
d
u
r
b
a
n
s
tu
d
ies,
th
er
e
h
as b
ee
n
lim
ited
atten
tio
n
g
iv
e
n
to
th
e
co
n
n
ec
tio
n
b
etwe
en
cr
im
e
an
d
q
u
alit
y
o
f
life
,
as
n
o
ted
b
y
[
8
]
.
N
o
tab
ly
,
in
v
esti
g
atio
n
s
in
to
th
e
in
ter
s
ec
tio
n
o
f
q
u
ality
o
f
life
an
d
cr
im
e
h
a
v
e
b
ee
n
co
n
d
u
cted
b
y
s
ev
er
al
r
esear
ch
er
s
in
clu
d
in
g
[
8
]
–
[
1
5
]
.
Fu
r
th
er
m
o
r
e
,
n
u
m
er
o
u
s
s
tu
d
ies,
in
clu
d
in
g
R
an
s
o
n
[
1
6
]
af
f
ir
m
a
co
r
r
elatio
n
b
etwe
en
w
ea
th
er
an
d
cr
im
e.
W
ea
th
er
’
s
p
iv
o
tal
r
o
le
in
cr
im
e
p
r
ed
ictio
n
is
u
n
d
e
r
lin
ed
,
with
h
y
p
o
th
eses
p
r
o
p
o
s
i
n
g
th
at
wea
th
er
ca
n
im
p
ac
t
s
o
cial
in
ter
ac
tio
n
-
r
ela
ted
cr
im
e
r
ates.
An
d
er
s
o
n
[
1
7
]
co
n
n
e
ct
wea
th
er
with
cr
im
e
p
r
o
d
u
ctio
n
an
d
B
ec
k
er
’
s
m
o
d
el,
wh
ile
ex
ter
n
a
l
f
ac
to
r
s
lik
e
h
ea
t
ca
n
in
f
lu
en
ce
ag
g
r
ess
io
n
.
R
esear
ch
b
y
R
a
n
s
o
n
[
1
6
]
r
ei
n
f
o
r
ce
th
e
lin
k
b
etwe
en
tem
p
er
atu
r
e
an
d
ag
g
r
ess
io
n
.
C
o
h
n
[
1
8
]
ac
k
n
o
wled
g
es
wea
th
er
’
s
r
o
le
in
c
r
im
e
th
eo
r
ies
an
d
it
Evaluation Warning : The document was created with Spire.PDF for Python.
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I
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tif
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tell
,
Vo
l.
13
,
No
.
4
,
Dec
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b
er
2
0
2
4
:
4
5
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-
4
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1
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4510
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s
b
eh
av
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r
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f
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T
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n
b
e
h
av
io
r
h
as
g
ar
n
er
ed
atten
tio
n
[
1
8
]
,
[
1
9
]
,
h
ig
h
lig
h
tin
g
its
r
elev
an
ce
in
c
r
im
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1
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6
0
s
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Sev
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esear
ch
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later
s
tu
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f
o
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cr
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in
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b
e
h
av
io
r
an
d
p
s
y
ch
o
lo
g
y
[
1
8
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[
2
0
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.
R
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s
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g
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ests
a
d
ir
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t
li
n
k
b
etwe
en
wea
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p
s
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ch
o
lo
g
ical
tr
i
g
g
er
s
f
o
r
v
io
len
t
cr
im
e
[
2
1
]
,
with
tr
ad
itio
n
al
b
elief
s
ass
o
ciatin
g
h
ig
h
h
u
m
id
ity
an
d
tem
p
er
atu
r
e
with
ag
g
r
ess
io
n
[
2
2
]
.
T
em
p
e
r
atu
r
e
’
s
in
f
lu
en
ce
o
n
cr
im
e,
esp
ec
ially
v
io
len
t
c
r
im
e,
is
a
k
ey
f
o
cu
s
[
1
6
]
,
with
h
i
g
h
er
tem
p
er
atu
r
es
co
r
r
elatin
g
to
in
cr
ea
s
ed
cr
im
e
r
ates
[
1
6
]
.
Ma
x
tem
p
er
at
u
r
e
ex
p
lain
s
s
ea
s
o
n
al
s
h
if
ts
in
v
io
len
t
cr
im
e
[
1
8
]
,
[
2
1
]
,
[
2
3
]
,
tr
ac
in
g
b
ac
k
to
h
eig
h
ten
e
d
ag
g
r
ess
io
n
in
s
u
m
m
e
r
[
2
1
]
.
V
io
len
t
cr
im
e
clo
s
ely
co
n
n
ec
ts
with
tem
p
er
atu
r
e,
u
n
lik
e
n
o
n
-
v
io
len
t
cr
im
e
[
2
1
]
.
Ma
x
tem
p
er
atu
r
e
an
d
h
i
g
h
h
u
m
id
ity
lin
k
s
tr
o
n
g
ly
to
v
i
o
len
t
cr
im
e,
wh
er
ea
s
m
in
an
d
av
er
a
g
e
tem
p
er
atu
r
e
h
a
v
e
less
im
p
ac
t
[
2
1
]
.
W
ea
th
er
s
ig
n
if
ican
tly
in
f
lu
e
n
ce
s
h
u
m
an
b
eh
av
io
r
,
p
o
ten
tia
lly
af
f
ec
tin
g
c
r
im
in
al
ac
tio
n
s
s
tated
b
y
J
u
n
g
et
a
l.
[
2
3
]
.
W
ea
th
er
c
o
n
d
itio
n
s
lik
e
tem
p
er
at
u
r
e,
r
ain
,
a
n
d
win
d
im
p
ac
t
o
u
t
d
o
o
r
ac
tiv
i
ty
an
d
in
ter
ac
tio
n
s
,
in
f
lu
en
cin
g
o
p
p
o
r
tu
n
ities
f
o
r
in
ter
p
er
s
o
n
al
cr
im
es
[
2
2
]
.
H
e
at
h
y
p
o
th
esis
s
u
g
g
ests
h
ig
h
tem
p
er
atu
r
es
tr
ig
g
er
ag
g
r
ess
iv
e
b
e
h
av
io
r
[
1
7
]
,
[
1
9
]
.
W
ea
th
er
s
h
a
p
es
cr
im
e
p
att
er
n
s
;
“
p
leasan
t
”
wea
th
er
k
ee
p
s
p
eo
p
le
in
d
o
o
r
s
,
r
ed
u
cin
g
cr
im
e
ch
an
ce
s
with
c
ap
ab
le
g
u
ar
d
ian
s
p
r
esen
t.
I
n
c
o
n
tr
ast,
in
clem
en
t
wea
th
er
d
eter
s
cr
im
in
als d
u
e
to
d
is
co
m
f
o
r
t
a
n
d
c
o
s
ts
,
af
f
ec
tin
g
b
u
r
g
lar
y
attem
p
ts
an
d
s
to
len
g
o
o
d
s
tr
an
s
p
o
r
tatio
n
[
2
4
]
.
“
Pleasan
t
”
wea
th
er
is
lin
k
ed
to
in
c
r
ea
s
ed
p
r
o
p
er
ty
cr
im
e
[
2
4
]
.
T
h
is
s
tu
d
y
ex
p
an
d
s
o
n
p
r
ev
io
u
s
r
esear
ch
b
y
in
v
esti
g
atin
g
th
e
im
p
ac
t
o
f
p
r
o
p
er
t
y
cr
im
e
o
n
q
u
ality
o
f
life
in
tr
o
p
ical
clim
ate
r
eg
i
o
n
s
.
Un
lik
e
ea
r
lier
i
n
v
esti
g
atio
n
s
co
n
d
u
cted
b
y
[
8
]
–
[
1
5
]
wh
ich
e
m
p
lo
y
e
d
co
n
v
en
tio
n
al
s
tatis
tical
m
eth
o
d
s
,
th
e
ad
v
an
ce
m
e
n
t
o
f
m
ac
h
in
e
lear
n
in
g
d
ee
m
b
en
ef
icial.
Ma
ch
in
e
lear
n
in
g
alg
o
r
ith
m
s
ar
e
co
n
s
tan
tly
u
p
g
r
ad
ed
[
2
5
]
–
[
2
7
]
,
p
r
o
m
is
in
g
g
r
e
at
p
o
ten
tial
in
wid
e
d
o
m
ain
s
o
f
r
ea
l
life
p
r
o
b
lem
s
.
T
h
is
s
tu
d
y
s
ig
n
if
ican
tly
b
u
ild
s
u
p
o
n
ex
is
tin
g
q
u
ality
-
of
-
life
r
esear
ch
b
y
in
tr
o
d
u
cin
g
a
n
o
v
el
ap
p
r
o
ac
h
in
v
o
lv
in
g
th
e
im
p
lem
en
tatio
n
o
f
m
ac
h
in
e
lear
n
in
g
p
r
e
d
ictio
n
.
T
h
r
o
u
g
h
th
e
u
tili
za
tio
n
o
f
t
h
r
ee
k
e
y
c
o
n
s
tr
u
cts,
th
is
ap
p
r
o
ac
h
aim
s
to
en
h
an
ce
o
u
r
cu
r
r
en
t
c
o
m
p
r
eh
en
s
io
n
o
f
th
e
co
n
n
ec
tio
n
s
an
d
ass
o
ciatio
n
s
b
etwe
en
q
u
ality
o
f
life
,
cr
im
e
,
an
d
wea
th
er
.
B
y
in
co
r
p
o
r
atin
g
m
ac
h
i
n
e
lear
n
in
g
tech
n
iq
u
es,
th
is
r
esear
ch
s
ee
k
s
to
p
r
o
v
id
e
a
m
o
r
e
s
o
p
h
is
ticated
an
d
co
m
p
r
eh
en
s
iv
e
u
n
d
er
s
tan
d
in
g
o
f
h
o
w
th
ese
v
ar
iab
les
in
ter
ac
t
an
d
in
f
lu
en
ce
o
n
e
an
o
th
er
.
T
h
is
a
p
p
r
o
ac
h
h
o
ld
s
th
e
p
o
ten
tial
to
u
n
c
o
v
er
n
u
an
ce
d
in
s
ig
h
ts
th
at
tr
ad
itio
n
al
r
esea
r
ch
m
et
h
o
d
s
m
ig
h
t
n
o
t
ca
p
tu
r
e
,
th
u
s
co
n
tr
ib
u
tin
g
t
o
a
d
ee
p
e
r
ex
p
l
o
r
atio
n
o
f
th
e
i
n
tr
icate
r
elatio
n
s
h
ip
s
b
etwe
en
q
u
ality
o
f
life
,
c
r
im
e
p
atter
n
s
,
an
d
wea
th
e
r
co
n
d
itio
n
s
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
2
.
1
.
Da
t
a
c
o
llect
io
n a
nd
da
t
a
s
et
s
Data
o
f
th
is
s
tu
d
y
wer
e
co
llected
u
s
in
g
q
u
esti
o
n
n
air
e
s
u
r
v
ey
th
at
co
m
p
r
is
es
o
f
q
u
ality
o
f
life
,
d
o
m
ain
s
.
T
h
e
s
ec
tio
n
o
f
q
u
esti
o
n
n
air
e
was
d
ev
elo
p
ed
b
ased
o
n
t
h
e
th
r
ee
co
n
s
tr
u
cts
o
f
th
e
q
u
ality
o
f
life
:
p
h
y
s
ical,
p
s
y
ch
o
lo
g
ical,
an
d
s
o
cial.
T
o
m
ea
s
u
r
e
ea
ch
co
n
s
tr
u
ct
o
f
th
i
s
s
tu
d
y
,
a
f
iv
e
-
p
o
in
t
L
ik
er
t
s
ca
le
was
em
p
lo
y
ed
,
r
an
g
in
g
f
r
o
m
1
=stro
n
g
ly
d
is
a
g
r
ee
to
5
=stro
n
g
ly
a
g
r
ee
.
E
s
t
im
ate
f
o
r
ea
ch
c
o
n
s
tr
u
ct
was
o
b
tain
ed
u
s
in
g
th
e
av
er
ag
e
v
al
u
es o
f
its
in
d
icato
r
s
.
T
h
e
q
u
esti
o
n
n
air
e
was
ad
m
i
n
is
ter
ed
to
th
e
r
esid
en
tial
ar
ea
r
esid
en
ts
f
r
o
m
a
n
eig
h
b
o
u
r
h
o
o
d
ar
ea
at
T
am
an
Dato
’
Sen
u
,
Sen
tu
l
K
u
ala
L
u
m
p
u
r
.
T
h
e
s
u
r
v
e
y
was
ca
r
r
ied
o
u
t
b
y
f
ac
e
to
f
ac
e
b
ec
au
s
e
th
is
m
eth
o
d
s
o
lv
ed
s
o
m
e
p
r
o
b
lem
s
o
n
th
e
s
ite,
s
u
ch
as
co
llectin
g
d
at
a
o
n
th
e
q
u
ality
o
f
life
,
allo
win
g
q
u
esti
o
n
in
t
h
e
q
u
esti
o
n
n
air
e
to
b
e
d
eliv
er
e
d
i
n
v
ar
io
u
s
way
s
u
n
til
th
e
r
esp
o
n
d
en
ts
u
n
d
e
r
s
tan
d
th
e
r
e
q
u
ir
e
m
en
ts
o
f
th
e
q
u
esti
o
n
an
d
e
n
s
u
r
e
th
at
th
e
i
n
s
tr
u
ctio
n
in
th
e
s
u
r
v
e
y
ar
e
f
o
llo
wed
.
F
r
o
m
th
e
to
tal
o
f
3
1
7
q
u
esti
o
n
n
ai
r
e
ad
m
i
n
is
ter
ed
,
2
5
4
v
ali
d
r
esp
o
n
s
es we
r
e
u
s
ed
f
o
r
th
e
an
aly
s
is
,
r
ep
r
esen
tin
g
a
r
esp
o
n
s
e
r
ate
o
f
8
0
%.
2
.
2
.
Co
rr
el
a
t
io
ns
o
f
v
a
ria
ble
s
T
ab
le
1
lis
ts
th
e
in
d
ep
en
d
en
t
v
ar
iab
les
(
I
Vs)
in
p
r
ed
ictin
g
t
h
e
q
u
ality
o
f
life
n
am
el
y
p
r
o
p
er
ty
cr
im
e
an
d
tem
p
e
r
atu
r
e
.
B
ased
o
n
p
e
ar
s
o
n
co
r
r
elatio
n
test
,
th
e
p
r
o
p
er
ty
cr
im
e
p
r
esen
ts
a
m
o
d
er
at
e
r
elatio
n
s
h
ip
to
th
e
q
u
ality
o
f
life
wh
ile
tem
p
e
r
atu
r
e
h
as
v
er
y
lo
w
c
o
r
r
elatio
n
.
Alth
o
u
g
h
with
th
e
wea
k
ass
o
ciatio
n
,
it
is
in
ter
esti
n
g
to
o
b
s
er
v
e
h
o
w
th
e
tem
p
er
atu
r
e
ca
n
co
n
tr
i
b
u
te
s
o
m
e
in
f
lu
en
c
es o
n
d
if
f
er
en
t m
ac
h
i
n
e
lear
n
i
n
g
alg
o
r
ith
m
s
.
T
h
e
p
ea
r
s
o
n
c
o
r
r
elatio
n
co
e
f
f
icien
t
o
f
0
.
6
3
8
b
etwe
en
p
r
o
p
e
r
ty
cr
im
e
an
d
q
u
ality
o
f
life
i
n
d
icate
s
a
m
o
d
er
ate
p
o
s
itiv
e
lin
ea
r
r
elati
o
n
s
h
ip
b
etwe
en
th
ese
two
v
ar
i
ab
les.
As
s
ee
n
in
T
a
b
le
1
,
a
v
al
u
e
o
f
0
.
6
3
8
in
d
icate
s
a
n
o
tab
le
d
eg
r
ee
o
f
ass
o
ciatio
n
b
etwe
en
p
r
o
p
er
ty
cr
im
e
an
d
q
u
ality
o
f
life
,
s
u
g
g
esti
n
g
th
at
ar
ea
s
o
r
lo
ca
tio
n
with
h
ig
h
er
p
r
o
p
er
ty
cr
im
e
r
a
tes
ten
d
to
ex
h
ib
it
lo
wer
q
u
al
ity
o
f
life
r
atin
g
s
,
an
d
ar
ea
s
with
lo
wer
p
r
o
p
er
ty
cr
im
e
r
ates te
n
d
to
h
av
e
h
ig
h
e
r
q
u
ality
o
f
life
r
atin
g
s
.
T
h
e
p
r
o
p
er
ty
cr
im
e
i
n
d
ex
will e
n
co
m
p
ass
,
s
u
ch
as th
ef
t,
ca
r
/v
eh
icle
th
ef
t,
m
o
to
r
cy
cle
th
ef
t,
lo
r
r
y
/v
an
/b
u
s
th
ef
t,
s
n
atch
th
ef
t,
an
d
b
u
r
g
lar
y
.
T
h
e
f
o
cu
s
will
b
e
o
n
u
n
d
er
s
tan
d
i
n
g
th
e
r
elatio
n
s
h
ip
b
etwe
en
tem
p
er
atu
r
e
c
h
an
g
es
,
th
e
o
cc
u
r
r
e
n
ce
o
f
c
r
im
in
al
ac
tiv
ity
,
an
d
th
e
lev
el
o
f
s
atis
f
ac
tio
n
o
f
r
esid
en
tial p
eo
p
le
r
eg
a
r
d
in
g
th
eir
q
u
ality
o
f
life
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
Ma
ch
in
e
lea
r
n
in
g
p
r
ed
ictio
n
o
f q
u
a
lity o
f life:
I
n
s
ig
h
t fro
m
p
r
o
p
erty c
r
ime
…
(
A
n
is
Zu
la
ikh
a
Mo
h
d
Zu
kri
)
4511
T
ab
le
1
.
Pear
s
o
n
c
o
r
r
elatio
n
o
f
ea
ch
I
V
to
th
e
d
e
p
en
d
e
n
t v
ar
iab
le
(
DV)
A
t
t
r
i
b
u
t
e
C
o
r
r
e
l
a
t
i
o
n
c
o
e
f
f
i
c
i
e
n
t
P
r
o
p
e
r
t
y
c
r
i
me
0
.
6
3
8
Te
mp
e
r
a
t
u
r
e
0
.
0
1
2
C
o
n
s
eq
u
en
tly
,
two
g
r
o
u
p
s
o
f
f
ea
tu
r
es we
r
e
s
elec
ted
f
o
r
th
e
m
ac
h
in
e
lear
n
in
g
m
o
d
el:
i
)
q
u
ality
o
f
life
with
p
r
o
p
er
ty
cr
im
e
an
d
tem
p
er
atu
r
e,
an
d
ii
)
q
u
ality
o
f
life
with
o
u
t
tem
p
er
atu
r
e.
T
h
ese
s
e
ts
o
f
f
ea
tu
r
es
wer
e
in
clu
d
ed
in
T
ab
le
2
to
f
ac
ilit
ate
th
e
m
ac
h
in
e
lear
n
in
g
p
r
o
ce
s
s
b
y
p
r
o
v
id
in
g
a
clea
r
d
if
f
er
en
tiatio
n
o
f
t
h
e
v
ar
iab
les u
tili
ze
d
f
o
r
a
n
aly
s
is
an
d
p
r
ed
ictio
n
.
Featu
r
e
s
elec
tio
n
in
v
o
lv
es c
h
o
o
s
in
g
a
s
et
o
f
p
er
tin
en
t f
ea
t
u
r
es o
r
attr
ib
u
tes
to
u
tili
ze
in
a
m
ac
h
i
n
e
lear
n
in
g
m
o
d
el.
T
h
e
f
ea
t
u
r
e
im
p
o
r
ta
n
ce
weig
h
ts
ca
lcu
late
d
f
o
r
ea
ch
f
ea
tu
r
e
in
ea
ch
alg
o
r
ith
m
,
an
d
th
e
weig
h
t
s
in
d
icate
th
e
r
elativ
e
im
p
o
r
tan
ce
o
f
ea
ch
f
ea
tu
r
e
in
th
e
m
o
d
el
.
An
aly
s
in
g
f
ea
tu
r
e
im
p
o
r
tan
ce
weig
h
ts
en
a
b
les
a
d
ee
p
er
u
n
d
er
s
tan
d
in
g
o
f
th
e
f
ac
to
r
s
d
r
iv
in
g
th
e
m
o
d
el
’
s
p
r
ed
ictio
n
s
.
Featu
r
es
with
h
ig
h
er
im
p
o
r
tan
ce
weig
h
t
s
ar
e
d
ee
m
ed
to
h
av
e
a
m
o
r
e
s
u
b
s
tan
tial
im
p
ac
t
o
n
th
e
m
o
d
el
’
s
d
ec
is
io
n
-
m
ak
in
g
p
r
o
ce
s
s
,
wh
ile
th
o
s
e
with
lo
we
r
weig
h
ts
m
ay
co
n
tr
ib
u
te
less
s
ig
n
if
ican
tly
o
r
m
ay
ev
e
n
b
e
co
n
s
id
er
ed
ir
r
elev
an
t.
T
h
is
in
f
o
r
m
atio
n
is
in
s
tr
u
m
en
t
al
in
co
m
p
r
e
h
en
d
in
g
h
o
w
th
e
m
o
d
el
lev
er
a
g
es
v
ar
io
u
s
f
ea
tu
r
es
to
m
ak
e
ac
cu
r
ate
p
r
ed
ictio
n
s
,
th
er
e
b
y
aid
i
n
g
in
m
o
d
el
in
ter
p
r
etatio
n
an
d
v
alid
atio
n
.
T
ab
le
2
.
Featu
r
es selectio
n
g
r
o
u
p
s
f
o
r
th
e
m
ac
h
in
e
lear
n
i
n
g
G
r
o
u
p
I
V
s
DV
F
e
a
t
u
r
e
s
e
l
e
c
t
i
o
n
1
P
r
o
p
e
r
t
y
c
r
i
me,
a
n
d
t
e
mp
e
r
a
t
u
r
e
Q
u
a
l
i
t
y
o
f
l
i
f
e
F
e
a
t
u
r
e
s
e
l
e
c
t
i
o
n
2
P
r
o
p
e
r
t
y
c
r
i
me
Q
u
a
l
i
t
y
o
f
l
i
f
e
2
.
3
.
M
a
chine
lea
rning
T
h
er
e
wer
e
s
ix
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
h
as
b
ee
n
s
u
g
g
ested
b
y
R
ap
id
Min
er
A
u
to
M
o
d
el
f
o
r
th
e
q
u
ality
-
of
-
life
d
ataset.
Ho
wev
er
,
o
n
ly
f
iv
e
m
ac
h
in
e
lear
n
i
n
g
alg
o
r
ith
m
s
n
am
ely
g
e
n
er
a
lized
lin
ea
r
m
o
d
el
(
GL
M)
[
2
8
]
,
r
a
n
d
o
m
f
o
r
est
(
R
F)
[
2
9
]
,
a
n
d
d
ec
is
io
n
tr
ee
(
DT
)
[
2
9
]
,
g
r
ad
ien
t
b
o
o
s
ted
tr
ee
s
(
GB
T
)
[
2
9
]
,
an
d
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SVM)
[
3
0
]
h
av
e
b
ee
n
ch
o
s
en
f
o
r
th
is
s
tu
d
y
.
Dee
p
lear
n
in
g
was
ex
clu
d
ed
d
u
e
to
co
m
p
lex
ity
o
f
alg
o
r
ith
m
th
a
t
ca
u
s
es
lo
n
g
p
r
o
ce
s
s
in
g
tim
e
to
co
m
p
lete.
T
ab
le
3
o
u
tlin
es
th
e
o
p
tim
al
h
y
p
er
-
p
ar
am
eter
s
o
f
ea
ch
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
f
r
o
m
th
e
p
r
elim
in
ar
y
m
ac
h
in
e
lea
r
n
i
n
g
h
y
p
er
-
p
ar
am
eter
s
tu
n
in
g
.
T
ab
le
3
.
C
o
n
f
i
g
u
r
atio
n
o
f
p
ar
a
m
eter
s
A
l
g
o
r
i
t
h
m
O
p
t
i
mal
p
a
r
a
m
e
t
e
r
s
Er
r
o
r
r
a
t
e
(
%
)
RF
N
u
mb
e
r
o
f
t
r
e
e
s=
6
0
M
a
x
i
m
a
l
d
e
p
t
h
=
7
6
.
6
DT
M
a
x
i
m
a
l
d
e
p
t
h
=
7
6
.
8
G
B
T
N
u
mb
e
r
o
f
Tr
e
e
s=
3
0
M
a
x
i
m
a
l
d
e
p
t
h
=
7
Le
a
r
n
i
n
g
r
a
t
e
=
0
.
1
0
0
7
.
0
S
V
M
K
e
r
n
a
l
G
a
mm
a
=
0
.
0
5
0
C
=
1
,
0
0
0
5
.
8
T
h
e
n
u
m
b
er
o
f
tr
ee
s
u
s
ed
in
th
e
p
r
elim
in
ar
y
h
y
p
er
-
p
ar
am
eter
s
tu
n
in
g
o
f
R
F a
r
e
2
0
,
6
0
,
1
0
0
,
1
4
0
.
Fo
r
ea
ch
o
f
th
e
f
o
u
r
n
u
m
b
er
o
f
tr
e
es,
th
r
ee
v
alu
es
o
f
m
ax
im
al
d
e
p
th
(
2
,
4,
7
)
h
a
v
e
b
ee
n
o
b
s
er
v
ed
.
T
h
e
wo
r
s
t
er
r
o
r
r
ate
was
7
.
3
%
with
th
e
n
u
m
b
e
r
o
f
tr
ee
s
e
q
u
als
2
0
,
60,
1
0
0
,
1
4
0
a
n
d
it
i
s
m
ax
im
al
d
ep
th
wa
s
2
.
T
h
e
b
est
er
r
o
r
r
ate
is
6
.
6
% with
th
e
co
n
f
ig
u
r
atio
n
g
iv
en
in
T
ab
le
2
.
Fo
r
th
e
DT
,
th
e
r
a
n
g
e
o
f
m
ax
i
m
al
d
ep
th
u
s
ed
in
th
e
p
r
elim
in
ar
y
test
in
g
is
b
etwe
en
2
t
o
2
5
.
T
h
e
h
ig
h
est
er
r
o
r
r
ate
was 7
.
7
% if
th
e
m
ax
im
al
d
ep
th
is
2
,
wh
ich
ca
n
b
e
r
ed
u
ce
d
to
7
.
0
% with
m
ax
im
al
d
ep
th
4
.
T
h
e
er
r
o
r
r
ate
v
alu
e
r
em
ai
n
ed
co
n
s
is
ten
t to
6
.
8
% wh
e
n
t
h
e
m
a
x
im
al
d
e
p
th
was set to
7
,
1
0
,
15
,
o
r
2
5
.
GB
T
h
as
ad
d
itio
n
al
p
ar
am
eter
n
am
ely
lear
n
in
g
r
ate
b
esid
es n
u
m
b
er
o
f
tr
ee
s
a
n
d
m
ax
im
al
d
ep
th
.
T
h
e
m
in
im
u
m
n
u
m
b
er
o
f
tr
ee
s
u
s
ed
in
th
e
p
r
elim
in
ar
y
alg
o
r
it
h
m
tu
n
in
g
is
3
0
an
d
th
e
m
a
x
im
u
m
is
1
5
0
with
2,
4
,
an
d
7
alter
n
ativ
es
o
f
m
a
x
im
al
d
ep
t
h
.
T
h
e
s
er
ies
o
f
t
h
e
lear
n
in
g
r
ate
was
s
et
b
etwe
e
n
0
.
0
0
1
to
0
.
1
.
T
h
e
h
ig
h
est
er
r
o
r
r
ate
ac
h
iev
e
d
is
1
1
.
6
%
with
3
0
n
u
m
b
er
o
f
tr
ee
s
,
2
m
a
x
im
al
d
e
p
th
a
n
d
0
.
0
0
1
lear
n
in
g
r
ate.
T
h
e
lo
west
er
r
o
r
(
6
.
6
%)
ca
n
b
e
o
b
s
er
v
ed
wh
e
n
th
e
n
u
m
b
e
r
o
f
tr
ee
s
r
em
ain
3
0
b
u
t
th
e
m
ax
im
al
d
e
p
th
an
d
th
e
lear
n
in
g
r
ate
wer
e
s
et
to
4
an
d
0
.
1
r
esp
ec
tiv
ely
.
SVM
u
s
es
Ker
n
al
Gam
m
a
an
d
C
(
r
eg
u
lar
izatio
n
)
p
ar
am
ete
r
s
,
wh
ich
wer
e
o
b
s
er
v
ed
in
th
e
p
r
elim
in
ar
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r
esear
ch
b
etwe
en
0
.
0
0
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to
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f
o
r
Ker
n
al
Gam
m
a
an
d
1
0
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1
0
0
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o
r
C
.
T
h
e
wo
r
s
e
s
ettin
g
g
e
n
er
a
ted
b
y
SVM
wh
en
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ased
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id
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h
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ef
o
r
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th
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ata,
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o
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t
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wer
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s
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o
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ac
h
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n
e
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n
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g
tr
ain
i
n
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d
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wer
e
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s
ed
in
th
e
m
ac
h
in
e
lear
n
i
n
g
t
esti
n
g
.
3.
RE
SU
L
T
S
A
ND
D
I
SCU
SS
I
O
N
T
h
is
r
esear
ch
p
r
esen
ts
two
d
is
tin
ct
s
ets
o
f
r
esu
lts
.
F
ir
s
tly
,
T
a
b
le
4
o
u
tlin
es
th
e
p
er
f
o
r
m
a
n
ce
o
u
tco
m
es
o
f
m
ac
h
in
e
lear
n
in
g
in
p
r
ed
ict
in
g
th
e
q
u
ality
o
f
life
.
T
h
is
tab
le
ca
n
b
e
u
s
ed
to
co
m
p
ar
e
ea
ch
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
p
er
f
o
r
m
an
ce
in
ac
c
o
r
d
an
ce
with
th
e
two
-
f
ea
tu
r
e
s
elec
tio
n
as
s
h
o
wn
in
T
ab
le
2
.
Seco
n
d
ly
,
Fig
u
r
e
1
p
r
esen
t in
d
ep
th
th
e
p
r
esen
tatio
n
an
d
d
is
cu
s
s
io
n
o
f
h
o
w
th
e
m
ac
h
in
e
lear
n
in
g
al
g
o
r
ith
m
s
a
r
e
in
f
lu
e
n
ce
d
b
y
t
h
e
two
IV
,
p
r
o
p
e
r
ty
cr
im
e
an
d
te
m
p
er
atu
r
e
.
T
h
e
an
aly
s
is
o
f
th
e
s
e
r
esu
lts
p
r
o
v
id
es
a
co
m
p
r
eh
e
n
s
iv
e
u
n
d
er
s
tan
d
in
g
o
f
th
e
i
n
ter
p
lay
b
etwe
en
m
ac
h
in
e
lear
n
in
g
,
q
u
ality
o
f
life
p
r
ed
ictio
n
,
an
d
th
e
im
p
ac
t
o
f
p
r
o
p
er
t
y
cr
im
e
an
d
tem
p
er
atu
r
e
as
IV
.
T
ab
le
4
.
T
h
e
p
er
f
o
r
m
a
n
ce
s
r
esu
lt
A
l
g
o
r
i
t
h
m
R
M
S
E
(+
/
-
s
t
d
.
d
e
v
)
R^
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st
d
.
d
e
v
)
Ti
me
t
o
c
o
m
p
l
e
t
e
(
s)
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e
a
t
u
r
e
S
e
l
e
c
t
i
o
n
1
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LM
0
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3
4
5
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6
1
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84
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V
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3
3
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76
F
e
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t
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r
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8
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3
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6
1
3
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7
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2
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5
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87
S
V
M
4
.
3
6
3
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0
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6
8
4
(
+
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.
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9
4
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44
Fig
u
r
e
1
.
W
eig
h
t o
f
I
Vs in
m
a
ch
in
e
lear
n
in
g
alg
o
r
ith
m
Alth
o
u
g
h
tem
p
er
atu
r
e
ex
h
ib
its
a
lo
w
co
r
r
elatio
n
ac
co
r
d
in
g
to
th
e
Pear
s
o
n
test
wh
en
co
n
s
id
er
ed
o
u
ts
id
e
o
f
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
,
it
i
s
u
til
ity
in
en
h
an
cin
g
p
r
e
d
ictiv
e
ab
ilit
ies
b
ec
o
m
es
ap
p
ar
en
t
with
in
ce
r
tain
alg
o
r
ith
m
s
.
Desp
ite
th
e
i
n
itial
lo
w
co
r
r
elatio
n
v
alu
e,
T
e
m
p
e
r
atu
r
e
m
a
n
ag
es
to
c
o
n
tr
ib
u
te
v
alu
ab
le
in
s
ig
h
ts
to
0.00
0.20
0.40
0.60
0.80
1.00
1.20
G
L
M
DT
RF
G
BT
S
V
M
W
e
i
g
h
t
M
a
c
h
in
e
L
e
a
rn
in
g
A
lg
o
rit
h
m
P
ro
p
erty
Cri
me
Tem
p
e
ra
t
u
re
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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8
9
3
8
Ma
ch
in
e
lea
r
n
in
g
p
r
ed
ictio
n
o
f q
u
a
lity o
f life:
I
n
s
ig
h
t fro
m
p
r
o
p
erty c
r
ime
…
(
A
n
is
Zu
la
ikh
a
Mo
h
d
Zu
kri
)
4513
s
p
ec
if
ic
alg
o
r
ith
m
s
,
lead
in
g
t
o
im
p
r
o
v
em
e
n
ts
in
t
h
eir
p
r
e
d
ictiv
e
ac
cu
r
ac
y
.
T
h
is
s
h
o
ws
h
o
w
d
etailed
a
n
d
co
m
p
licated
its
im
p
ac
t
ca
n
b
e
in
th
e
co
n
tex
t
o
f
m
ac
h
in
e
lear
n
in
g
.
E
v
en
f
ac
to
r
s
th
at
m
a
y
s
ee
m
u
n
r
elate
d
ca
n
b
e
im
p
o
r
tan
t a
n
d
im
p
r
o
v
e
p
r
e
d
ictio
n
r
esu
lts
.
I
n
te
r
m
s
o
f
p
r
ed
ict
iv
e
ac
cu
r
ac
y
,
th
e
r
o
o
t m
ea
n
s
q
u
ar
e
er
r
o
r
(
R
MSE
)
o
f
f
er
s
a
m
ea
s
u
r
e
o
f
h
o
w
clo
s
e
th
e
m
o
d
el'
s
p
r
ed
ictio
n
s
ar
e
to
th
e
ac
tu
al
v
alu
es.
L
o
wer
R
MSE
v
alu
es
in
d
icate
b
etter
p
r
ed
ictiv
e
p
e
r
f
o
r
m
an
ce
.
Am
o
n
g
th
e
alg
o
r
ith
m
s
,
th
e
D
T
ex
h
ib
ited
th
e
lo
west
R
M
SE
o
f
0
.
3
2
(
+/
-
0
.
0
6
7
)
,
f
o
llo
wed
clo
s
ely
b
y
R
F
with
an
R
MSE
o
f
0
.
3
2
7
(
+/
-
0
.
0
3
8
)
.
T
h
ese
alg
o
r
ith
m
s
d
em
o
n
s
tr
a
ted
m
o
r
e
ac
cu
r
ate
p
r
ed
ictio
n
s
co
m
p
ar
ed
t
o
th
e
o
t
h
er
m
o
d
els.
T
h
e
co
ef
f
icien
t
o
f
d
eter
m
i
n
atio
n
(
R
2
)
m
ea
s
u
r
es
h
o
w
well
th
e
m
o
d
el
’
s
p
r
e
d
ictio
n
s
m
atch
t
h
e
ac
tu
al
d
ata.
A
h
ig
h
er
R
2
v
alu
e
in
d
ic
ates
b
etter
alig
n
m
en
t
b
etwe
en
p
r
ed
ictio
n
s
a
n
d
ac
tu
al
v
alu
es
.
I
n
t
h
is
ca
s
e,
SVM
led
with
th
e
h
ig
h
est
R
2
o
f
0
.
6
6
6
(
+/
-
0
.
2
9
4
)
,
in
d
icatin
g
th
at
i
t
ca
p
tu
r
ed
a
s
u
b
s
tan
tial
p
o
r
tio
n
o
f
th
e
v
ar
ian
ce
in
th
e
d
ata.
C
o
n
s
id
er
in
g
th
e
co
m
b
in
atio
n
o
f
th
ese
m
etr
ics,
p
ar
ticu
la
r
ly
t
h
e
R
MSE
,
an
d
R
2
,
SVM
em
er
g
es
as
th
e
b
est
-
p
er
f
o
r
m
in
g
alg
o
r
ith
m
in
p
r
ed
ictin
g
q
u
ality
o
f
life
.
T
h
e
h
ig
h
er
R
2
,
an
d
c
o
m
p
etitiv
e
R
MSE
in
d
icate
s
r
o
b
u
s
t
p
r
ed
ictiv
e
ca
p
a
b
ilit
ies
an
d
ac
cu
r
ate
r
esu
lts
.
Ho
wev
er
,
p
r
ac
tical
co
n
s
id
er
atio
n
s
s
u
ch
as
c
o
m
p
u
tatio
n
al
tim
e
s
h
o
u
ld
also
b
e
tak
en
in
to
ac
c
o
u
n
t
wh
en
s
elec
tin
g
th
e
m
o
s
t
s
u
itab
le
alg
o
r
ith
m
f
o
r
a
s
p
ec
if
ic
u
s
e
ca
s
e.
SVM
to
o
k
lo
n
g
er
tim
e
th
an
GL
M,
R
F
,
an
d
DT
b
u
t
s
h
o
r
ter
th
a
n
GB
T
.
Nev
er
th
eless
,
th
e
tim
e
tak
en
is
c
o
n
s
id
er
a
b
le
ac
ce
p
tab
le,
wh
ich
is
less
th
an
a
m
in
u
te
with
f
ea
tu
r
e
s
elec
tio
n
2
.
Fu
r
th
er
m
o
r
e
,
it
is
in
ter
esti
n
g
to
g
ain
in
s
ig
h
t
in
to
th
e
c
o
n
tr
i
b
u
tio
n
s
o
f
ea
c
h
attr
i
b
u
te
t
h
at
in
f
lu
en
ce
s
m
ac
h
in
e
lear
n
i
n
g
p
er
f
o
r
m
an
ce
.
Fig
u
r
e
1
p
r
esen
ts
th
e
weig
h
ts
o
f
th
e
co
n
tr
i
b
u
tio
n
s
ca
lcu
late
d
b
ased
o
n
p
ea
r
s
o
n
co
r
r
elatio
n
.
T
h
ese
co
n
tr
ib
u
tio
n
s
co
llectiv
ely
in
f
o
r
m
th
e
p
r
ed
ictiv
e
m
o
d
el
with
GL
M,
DT
,
R
F,
GB
T
,
an
d
SVM,
p
r
o
v
id
i
n
g
a
c
o
m
p
r
e
h
en
s
iv
e
u
n
d
er
s
tan
d
in
g
o
f
ea
c
h
attr
ib
u
te'
s
im
p
ac
t
o
n
th
e
o
v
er
all
p
r
e
d
ictiv
e
ac
cu
r
ac
y
ac
r
o
s
s
d
if
f
er
en
t a
lg
o
r
ith
m
s
.
T
h
e
r
esu
lts
in
Fig
u
r
e
1
s
h
o
w
f
iv
e
(
5
)
d
i
f
f
er
en
t
m
ac
h
in
e
lea
r
n
in
g
alg
o
r
ith
m
s
h
av
e
v
ar
y
i
n
g
lev
els
o
f
s
u
cc
ess
in
p
r
ed
ictin
g
q
u
ality
o
f
life
b
ased
o
n
p
r
o
p
er
ty
c
r
im
e
an
d
tem
p
er
at
u
r
e.
T
h
e
SVM
an
d
GB
T
m
o
d
els
s
ee
m
to
p
er
f
o
r
m
r
elativ
ely
well
in
ex
p
lain
in
g
t
h
e
v
ar
ia
b
ilit
y
in
p
r
o
p
er
ty
cr
im
e,
with
R
2
v
alu
es
o
f
0
.
6
7
a
n
d
0
.
6
6
,
r
esp
ec
tiv
ely
.
I
t
is
n
o
tewo
r
th
y
t
h
at
d
esp
ite
its
r
elativ
ely
l
o
w
in
f
lu
en
ce
,
T
e
m
p
er
atu
r
e
s
till
h
ad
a
d
is
ce
r
n
ib
le
ef
f
ec
t
o
n
th
e
q
u
ality
-
of
-
life
p
r
ed
ictio
n
s
m
ain
ly
in
R
F,
GB
T
,
a
n
d
SVM.
T
h
e
lo
w
in
f
lu
en
ce
tem
p
er
a
tu
r
e
in
th
is
an
al
y
s
is
is
in
lin
e
w
ith
th
e
Ma
lay
s
ia’
s
tr
o
p
ical
clim
ate,
wh
er
e
tem
p
er
atu
r
e
v
ar
iatio
n
s
m
ay
b
e
r
elativ
ely
co
n
s
is
ten
t
th
r
o
u
g
h
o
u
t t
h
e
y
ea
r
.
4.
CO
NCLU
SI
O
N
T
h
is
r
esear
ch
f
in
d
in
g
s
p
r
o
v
id
e
in
s
ig
h
t
in
to
th
e
p
er
f
o
r
m
a
n
ce
o
f
v
ar
io
u
s
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
in
p
r
ed
ictin
g
q
u
ality
o
f
life
.
T
h
e
an
aly
s
is
n
o
t
o
n
ly
ev
alu
a
tes
th
e
alg
o
r
ith
m
’
s
p
r
e
d
ictiv
e
ac
cu
r
ac
y
b
u
t
also
ex
p
lo
r
es
a
n
d
ex
am
in
e
in
to
h
o
w
th
e
IV
,
p
r
o
p
er
ty
cr
im
e
an
d
tem
p
e
r
atu
r
e
in
f
lu
e
n
ce
t
h
ese
alg
o
r
ith
m
s
.
I
t
d
em
o
n
s
tr
ates
th
at
th
e
im
p
ac
t
o
f
IV
ca
n
b
e
co
n
te
x
t
-
d
ep
e
n
d
e
n
t
an
d
th
at
SVM
is
a
s
tr
o
n
g
ca
n
d
id
ate
f
o
r
ac
cu
r
ate
p
r
ed
ictio
n
s
in
t
h
is
r
esear
ch
.
S
VM
s
tr
o
n
g
p
r
e
d
ictiv
e
ca
p
a
b
ilit
ies
an
d
ac
cu
r
ac
y
m
ak
e
it
th
e
p
r
ef
er
r
e
d
ch
o
ice
f
o
r
p
r
ed
ictin
g
q
u
ality
o
f
life
in
t
h
is
r
esear
ch
.
Ho
wev
er
,
th
e
s
elec
tio
n
o
f
th
e
s
u
itab
le
alg
o
r
i
th
m
s
h
o
u
ld
alwa
y
s
co
n
s
id
er
th
e
b
r
o
ad
e
r
co
n
tex
t,
i
n
clu
d
in
g
co
m
p
u
tatio
n
al
r
eso
u
r
ce
s
an
d
in
ter
p
r
etab
ilit
y
r
eq
u
ir
e
m
en
ts
.
Ho
wev
er
,
it
i
s
ess
en
tial to
co
n
s
id
er
t
h
e
co
n
tex
t a
n
d
th
e
g
o
als o
f
th
e
an
aly
s
is
wh
en
s
elec
tin
g
th
e
m
o
s
t a
p
p
r
o
p
r
iate
m
o
d
el,
as
f
ac
to
r
s
lik
e
m
o
d
el
i
n
ter
p
r
eta
b
ilit
y
,
co
m
p
u
tatio
n
al
r
eso
u
r
ce
s
,
an
d
o
t
h
er
d
o
m
ain
s
p
ec
if
ic
k
n
o
wled
g
e
also
p
la
y
a
r
o
le
in
ch
o
o
s
in
g
th
e
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1
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E.
H
.
M
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Ta
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2
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.
H
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.
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4
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5
]
R
.
M
.
G
.
N
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ma
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t
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.
,
“
T
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6
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R
.
J.
R
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g
e
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so
n
,
“
En
v
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me
n
t
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mi
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s,”
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l
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Me
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7
]
J.
G
y
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k
o
,
M
.
K
a
h
n
,
a
n
d
J.
Tr
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c
y
,
“
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p
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Re
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P
.
K
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A
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W
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N
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L.
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Evaluation Warning : The document was created with Spire.PDF for Python.